diff --git a/.github/workflows/pr-e2e-tests.yaml b/.github/workflows/pr-e2e-tests.yaml
index d4d29015..0480f907 100644
--- a/.github/workflows/pr-e2e-tests.yaml
+++ b/.github/workflows/pr-e2e-tests.yaml
@@ -15,7 +15,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.8', '3.12']
+        python-version: ['3.9', '3.12']
         neo4j-version:
           - 5
         neo4j-edition:
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 3bdc15e3..39c73472 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -6,7 +6,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ '3.8', '3.9', '3.10', '3.11', '3.12' ]
+        python-version: [ '3.9', '3.10', '3.11', '3.12' ]
     steps:
       - name: Install graphviz package
         run: sudo apt install graphviz graphviz-dev
diff --git a/.github/workflows/pre-release.yaml b/.github/workflows/pre-release.yaml
new file mode 100644
index 00000000..b60d3943
--- /dev/null
+++ b/.github/workflows/pre-release.yaml
@@ -0,0 +1,46 @@
+name: Publish a new Alpha release (x.y.0a0 -> x.y.0a1) 🚀
+
+on:
+  workflow_dispatch:
+
+jobs:
+  bump-version:
+    outputs:
+      version: ${{ steps.get-version.outputs.version }}
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          token: ${{ secrets.GIT_PUSH_PAT }}
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.9'
+
+      - name: Install and configure Poetry
+        uses: snok/install-poetry@v1
+        with:
+          version: 1.8.2
+          virtualenvs-create: false
+          virtualenvs-in-project: false
+          installer-parallel: true
+
+      - name: Bump version
+        run: poetry version prerelease
+
+      - name: Get version
+        id: get-version
+        run: echo version=`poetry version -s` >> "$GITHUB_OUTPUT"
+      - name: Print version
+        run: |
+          echo Version: ${{ steps.get-version.outputs.version }}
+      - name: Update CHANGELOG.md (cross platform supported)
+        run: |
+          sed -i.bak -e 's/## Next/## Next\n\n## ${{ steps.get-version.outputs.version }}/' CHANGELOG.md && rm CHANGELOG.md.bak
+      - uses: EndBug/add-and-commit@v9
+        with:
+          author_name: 'Neo4j-GraphRAG GitHub Action'
+          author_email: 'team-gen-ai@neo4j.com'
+          message: 'Bump version to ${{ steps.get-version.outputs.version }}'
+          add: "['pyproject.toml', 'CHANGELOG.md']"
+          tag: '${{ steps.get-version.outputs.version }}'
diff --git a/.github/workflows/premajor-release.yaml b/.github/workflows/premajor-release.yaml
new file mode 100644
index 00000000..6711988c
--- /dev/null
+++ b/.github/workflows/premajor-release.yaml
@@ -0,0 +1,46 @@
+name: Publish a new Alpha Major release (0.4.0 -> 1.0.0a0) 🚀
+
+on:
+  workflow_dispatch:
+
+jobs:
+  bump-version:
+    outputs:
+      version: ${{ steps.get-version.outputs.version }}
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          token: ${{ secrets.GIT_PUSH_PAT }}
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.9'
+
+      - name: Install and configure Poetry
+        uses: snok/install-poetry@v1
+        with:
+          version: 1.8.2
+          virtualenvs-create: false
+          virtualenvs-in-project: false
+          installer-parallel: true
+
+      - name: Bump version
+        run: poetry version premajor
+
+      - name: Get version
+        id: get-version
+        run: echo version=`poetry version -s` >> "$GITHUB_OUTPUT"
+      - name: Print version
+        run: |
+          echo Version: ${{ steps.get-version.outputs.version }}
+      - name: Update CHANGELOG.md (cross platform supported)
+        run: |
+          sed -i.bak -e 's/## Next/## Next\n\n## ${{ steps.get-version.outputs.version }}/' CHANGELOG.md && rm CHANGELOG.md.bak
+      - uses: EndBug/add-and-commit@v9
+        with:
+          author_name: 'Neo4j-GraphRAG GitHub Action'
+          author_email: 'team-gen-ai@neo4j.com'
+          message: 'Bump version to ${{ steps.get-version.outputs.version }}'
+          add: "['pyproject.toml', 'CHANGELOG.md']"
+          tag: '${{ steps.get-version.outputs.version }}'
diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml
index bb01c019..82387a96 100644
--- a/.github/workflows/publish.yaml
+++ b/.github/workflows/publish.yaml
@@ -12,7 +12,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.8"
+          python-version: "3.9"
       - name: Install pypa/build
         run: >-
           python3 -m
diff --git a/.github/workflows/scheduled-e2e-tests.yaml b/.github/workflows/scheduled-e2e-tests.yaml
index ff1ea0bc..59ddaa42 100644
--- a/.github/workflows/scheduled-e2e-tests.yaml
+++ b/.github/workflows/scheduled-e2e-tests.yaml
@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
+        python-version: ['3.9', '3.10', '3.11', '3.12']
         neo4j-version:
           - 5
         neo4j-edition:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 3b33b254..cc256216 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,13 +2,14 @@
 
 ## Next
 
+- Added `SinglePropertyExactMatchResolver` component allowing to merge entities with exact same property (e.g. name)
+
+## 0.7.0
+
 ### Added
 - Added AzureOpenAILLM and AzureOpenAIEmbeddings to support Azure served OpenAI models
 - Added `template` validation in `PromptTemplate` class upon construction.
-- `custom_prompt` arg is now converted to `Text2CypherTemplate` class within the `Text2CypherRetriever.get_search_results` method.
-- `Text2CypherTemplate` and `RAGTemplate` prompt templates now require `query_text` arg and will error if it is not present. Previous `query_text` aliases may be used, but will warn of deprecation.
 - Examples demonstrating the use of Mistral embeddings and LLM in RAG pipelines.
-- Fixed bug in `Text2CypherRetriever` using `custom_prompt` arg where the `search` method would not inject the `query_text` content.
 - Added feature to include kwargs in `Text2CypherRetriever.search()` that will be injected into a custom prompt, if provided.
 - Added validation to `custom_prompt` parameter of `Text2CypherRetriever` to ensure that `query_text` placeholder exists in prompt.
 - Introduced a fixed size text splitter component for splitting text into specified fixed size chunks with overlap. Updated examples and tests to utilize this new component.
@@ -20,14 +21,19 @@
 
 ### Fixed
 - Resolved import issue with the Vertex AI Embeddings class.
+- Fixed bug in `Text2CypherRetriever` using `custom_prompt` arg where the `search` method would not inject the `query_text` content.
+- `custom_prompt` arg is now converted to `Text2CypherTemplate` class within the `Text2CypherRetriever.get_search_results` method.
+- `Text2CypherTemplate` and `RAGTemplate` prompt templates now require `query_text` arg and will error if it is not present. Previous `query_text` aliases may be used, but will warn of deprecation.
 - Resolved issue where Neo4jWriter component would raise an error if the start or end node ID was not defined properly in the input.
 - Resolved issue where relationship types was not escaped in the insert Cypher query.
-- Improved query performance in Neo4jWriter.
+- Improved query performance in Neo4jWriter: created nodes now have a generic `__KGBuilder__` label and an index is created on the `__KGBuilder__.id` property. Moreover, insertion queries are now batched. Batch size can be controlled using the `batch_size` parameter in the `Neo4jWriter` component.
 
 ### Changed
 - Moved the Embedder class to the neo4j_graphrag.embeddings directory for better organization alongside other custom embedders.
 - Removed query argument from the GraphRAG class' `.search` method; users must now use `query_text`.
 - Neo4jWriter component now runs a single query to merge node and set its embeddings if any.
+- Nodes created by the `Neo4jWriter` now have an extra `__KGBuilder__` label. Nodes from the entity graph also have an `__Entity__` label.
+- Dropped support for Python 3.8 (end of life).
 
 ## 0.6.3
 ### Changed
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 42ad4749..c98c4891 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -54,7 +54,7 @@ Remember that many community members have become regular contributors and some a
 ## Specifically for this project
 Setting up the development environment:
 
-1. Install Python 3.8.1+
+1. Install Python 3.9+
 2. Install poetry (see https://python-poetry.org/docs/#installation)
 3. Install dependencies:
 
diff --git a/README.md b/README.md
index d32b1b60..6d728eef 100644
--- a/README.md
+++ b/README.md
@@ -14,13 +14,12 @@ Python versions supported:
 * Python 3.11 supported.
 * Python 3.10 supported.
 * Python 3.9 supported.
-* Python 3.8 supported.
 
 # Usage
 
 ## Installation
 
-This package requires Python (>=3.8.1).
+This package requires Python (>=3.9).
 
 To install the latest stable version, use:
 
@@ -37,6 +36,47 @@ Follow installation instructions [here](https://pygraphviz.github.io/documentati
 
 ## Examples
 
+### Knowledge graph construction
+
+```python
+from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline
+from neo4j_graphrag.llm.openai_llm import OpenAILLM
+
+# Instantiate Entity and Relation objects
+entities = ["PERSON", "ORGANIZATION", "LOCATION"]
+relations = ["SITUATED_AT", "INTERACTS", "LED_BY"]
+potential_schema = [
+    ("PERSON", "SITUATED_AT", "LOCATION"),
+    ("PERSON", "INTERACTS", "PERSON"),
+    ("ORGANIZATION", "LED_BY", "PERSON"),
+]
+
+# Instantiate the LLM
+llm = OpenAILLM(
+    model_name="gpt-4o",
+    model_params={
+        "max_tokens": 2000,
+        "response_format": {"type": "json_object"},
+    },
+)
+
+# Create an instance of the SimpleKGPipeline
+kg_builder = SimpleKGPipeline(
+    llm=llm,
+    driver=driver,
+    file_path=file_path,
+    entities=entities,
+    relations=relations,
+)
+
+await kg_builder.run_async(text="""
+    Albert Einstein was a German physicist born in 1879 who wrote many groundbreaking
+    papers especially about general relativity and quantum mechanics.
+""")
+```
+
+
+
 ### Creating a vector index
 
 When creating a vector index, make sure you match the number of dimensions in the index with the number of dimensions the embeddings have.
diff --git a/docs/source/api.rst b/docs/source/api.rst
index 0eafde26..6cde0c35 100644
--- a/docs/source/api.rst
+++ b/docs/source/api.rst
@@ -71,6 +71,14 @@ LLMEntityRelationExtractor
     :members: run
 
 
+SinglePropertyExactMatchResolver
+================================
+
+.. autoclass:: neo4j_graphrag.experimental.components.resolver.SinglePropertyExactMatchResolver
+    :members: run
+
+
+
 .. _pipeline-section:
 
 ********
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 9b9e51b3..bbc31633 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -27,7 +27,6 @@ Python versions supported:
 * Python 3.11
 * Python 3.10
 * Python 3.9
-* Python 3.8
 
 
 ******
@@ -60,7 +59,7 @@ Usage
 Installation
 ************
 
-This package requires Python (>=3.8.1).
+This package requires Python (>=3.9).
 
 To install the latest stable version, use:
 
@@ -302,4 +301,4 @@ Indices and tables
 
 * :ref:`genindex`
 * :ref:`modindex`
-* :ref:`search`
\ No newline at end of file
+* :ref:`search`
diff --git a/docs/source/user_guide_kg_builder.rst b/docs/source/user_guide_kg_builder.rst
index ce9a61fa..2b91eaff 100644
--- a/docs/source/user_guide_kg_builder.rst
+++ b/docs/source/user_guide_kg_builder.rst
@@ -11,8 +11,6 @@ unstructured data.
 
     This feature is still experimental. API changes and bug fixes are expected.
 
-    It is not recommended to use it in production yet.
-
 
 ******************
 Pipeline Structure
@@ -26,6 +24,7 @@ A Knowledge Graph (KG) construction pipeline requires a few components:
 - **Schema builder**: provide a schema to ground the LLM extracted entities and relations and obtain an easily navigable KG.
 - **Entity and relation extractor**: extract relevant entities and relations from the text.
 - **Knowledge Graph writer**: save the identified entities and relations.
+- **Entity resolver**: merge similar entities into a single node.
 
 .. image:: images/kg_builder_pipeline.png
   :alt: KG Builder pipeline
@@ -389,7 +388,13 @@ to a Neo4j database:
         graph = Neo4jGraph(nodes=[], relationships=[])
         await writer.run(graph)
 
-See :ref:`neo4jgraph` for the description of the input type.
+To improve insert performances, it is possible to act on two parameters:
+
+- `batch_size`: the number of nodes/relationships to be processed in each batch (default is 1000).
+- `max_concurrency`: the max number of concurrent queries (default is 5).
+
+See :ref:`neo4jgraph`.
+
 
 It is possible to create a custom writer using the `KGWriter` interface:
 
@@ -419,4 +424,45 @@ It is possible to create a custom writer using the `KGWriter` interface:
     The `validate_call` decorator is required when the input parameter contain a `pydantic` model.
 
 
-See :ref:`kgwritermodel` and :ref:`kgwriter` in API reference.
\ No newline at end of file
+See :ref:`kgwritermodel` and :ref:`kgwriter` in API reference.
+
+
+Entity Resolver
+===============
+
+The KG Writer component creates new nodes for each identified entity
+without making assumptions about entity similarity. The Entity Resolver
+is responsible for refining the created knowledge graph by merging entity
+nodes that represent the same real-world object.
+
+In practice, this package implements a single resolver that merges nodes
+with the same label and identical "name" property.
+
+.. warning::
+
+    The `SinglePropertyExactMatchResolver` **replaces** the nodes created by the KG writer.
+
+
+It can be used like this:
+
+.. code:: python
+    from neo4j_graphrag.experimental.components.resolver import (
+        SinglePropertyExactMatchResolver,
+    )
+    resolver = SinglePropertyExactMatchResolver(driver)
+    res = await resolver.run()
+
+.. warning::
+
+    By default, all nodes with the __Entity__ label will be resolved.
+    To exclude specific nodes, a filter_query can be added to the query.
+    For example, if a `:Resolved` label has been applied to already resolved entities
+    in the graph, these entities can be excluded with the following approach:
+
+    .. code:: python
+
+        from neo4j_graphrag.experimental.components.resolver import (
+            SinglePropertyExactMatchResolver,
+        )
+        resolver = SinglePropertyExactMatchResolver(driver, filter_query="WHERE not entity:Resolved")
+        res = await resolver.run()
diff --git a/examples/pipeline/Harry Potter and the Chamber of Secrets Summary.pdf b/examples/pipeline/Harry Potter and the Chamber of Secrets Summary.pdf
new file mode 100644
index 00000000..8fa942c3
Binary files /dev/null and b/examples/pipeline/Harry Potter and the Chamber of Secrets Summary.pdf differ
diff --git a/examples/pipeline/kg_builder_example.py b/examples/pipeline/kg_builder_example.py
new file mode 100644
index 00000000..1a5281de
--- /dev/null
+++ b/examples/pipeline/kg_builder_example.py
@@ -0,0 +1,86 @@
+#  Copyright (c) "Neo4j"
+#  Neo4j Sweden AB [https://neo4j.com]
+#  #
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#  #
+#      https://www.apache.org/licenses/LICENSE-2.0
+#  #
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+from __future__ import annotations
+
+import asyncio
+import logging
+
+import neo4j
+from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline
+from neo4j_graphrag.llm.openai_llm import OpenAILLM
+
+logging.basicConfig(level=logging.INFO)
+
+
+async def main(neo4j_driver: neo4j.Driver) -> None:
+    # Instantiate Entity and Relation objects
+    entities = ["PERSON", "ORGANIZATION", "HORCRUX", "LOCATION"]
+    relations = ["SITUATED_AT", "INTERACTS", "OWNS", "LED_BY"]
+    potential_schema = [
+        ("PERSON", "SITUATED_AT", "LOCATION"),
+        ("PERSON", "INTERACTS", "PERSON"),
+        ("PERSON", "OWNS", "HORCRUX"),
+        ("ORGANIZATION", "LED_BY", "PERSON"),
+    ]
+
+    # Instantiate the LLM
+    llm = OpenAILLM(
+        model_name="gpt-4o",
+        model_params={
+            "max_tokens": 2000,
+            "response_format": {"type": "json_object"},
+        },
+    )
+
+    # Create an instance of the SimpleKGPipeline
+    kg_builder_pdf = SimpleKGPipeline(
+        llm=llm,
+        driver=neo4j_driver,
+        entities=entities,
+        relations=relations,
+        potential_schema=potential_schema,
+        from_pdf=True,
+        on_error="RAISE",
+    )
+
+    # Run the knowledge graph building process asynchronously
+    pdf_file_path = "examples/pipeline/Harry Potter and the Death Hallows Summary.pdf"
+    pdf_result = await kg_builder_pdf.run_async(file_path=pdf_file_path)
+    print(f"PDF Processing Result: {pdf_result}")
+
+    # Create an instance of the SimpleKGPipeline for text input
+    kg_builder_text = SimpleKGPipeline(
+        llm=llm,
+        driver=neo4j_driver,
+        entities=entities,
+        relations=relations,
+        potential_schema=potential_schema,
+        from_pdf=False,
+        on_error="RAISE",
+    )
+
+    # Run the knowledge graph building process with text input
+    text_input = "John Doe lives in New York City."
+    text_result = await kg_builder_text.run_async(text=text_input)
+    print(f"Text Processing Result: {text_result}")
+
+    await llm.async_client.close()
+
+
+if __name__ == "__main__":
+    with neo4j.GraphDatabase.driver(
+        "bolt://localhost:7687", auth=("neo4j", "password")
+    ) as driver:
+        asyncio.run(main(driver))
diff --git a/examples/pipeline/kg_builder_from_pdf.py b/examples/pipeline/kg_builder_from_pdf.py
index 2d587383..a841b92b 100644
--- a/examples/pipeline/kg_builder_from_pdf.py
+++ b/examples/pipeline/kg_builder_from_pdf.py
@@ -33,12 +33,14 @@
     FixedSizeSplitter,
 )
 from neo4j_graphrag.experimental.pipeline.pipeline import PipelineResult
-from neo4j_graphrag.llm import OpenAILLM
+from neo4j_graphrag.llm import LLMInterface, OpenAILLM
 
 logging.basicConfig(level=logging.INFO)
 
 
-async def main(neo4j_driver: neo4j.Driver) -> PipelineResult:
+async def define_and_run_pipeline(
+    neo4j_driver: neo4j.AsyncDriver, llm: LLMInterface
+) -> PipelineResult:
     from neo4j_graphrag.experimental.pipeline import Pipeline
 
     # Instantiate Entity and Relation objects
@@ -86,13 +88,7 @@ async def main(neo4j_driver: neo4j.Driver) -> PipelineResult:
     pipe.add_component(SchemaBuilder(), "schema")
     pipe.add_component(
         LLMEntityRelationExtractor(
-            llm=OpenAILLM(
-                model_name="gpt-4o",
-                model_params={
-                    "max_tokens": 2000,
-                    "response_format": {"type": "json_object"},
-                },
-            ),
+            llm=llm,
             on_error=OnError.RAISE,
         ),
         "extractor",
@@ -127,8 +123,23 @@ async def main(neo4j_driver: neo4j.Driver) -> PipelineResult:
     return await pipe.run(pipe_inputs)
 
 
-if __name__ == "__main__":
-    with neo4j.GraphDatabase.driver(
+async def main() -> PipelineResult:
+    llm = OpenAILLM(
+        model_name="gpt-4o",
+        model_params={
+            "max_tokens": 2000,
+            "response_format": {"type": "json_object"},
+        },
+    )
+    driver = neo4j.AsyncGraphDatabase.driver(
         "bolt://localhost:7687", auth=("neo4j", "password")
-    ) as driver:
-        print(asyncio.run(main(driver)))
+    )
+    res = await define_and_run_pipeline(driver, llm)
+    await driver.close()
+    await llm.async_client.close()
+    return res
+
+
+if __name__ == "__main__":
+    res = asyncio.run(main())
+    print(res)
diff --git a/examples/pipeline/kg_builder_from_text.py b/examples/pipeline/kg_builder_from_text.py
index 15817e18..193de0d0 100644
--- a/examples/pipeline/kg_builder_from_text.py
+++ b/examples/pipeline/kg_builder_from_text.py
@@ -15,7 +15,6 @@
 from __future__ import annotations
 
 import asyncio
-import logging.config
 
 import neo4j
 from neo4j_graphrag.embeddings.openai import OpenAIEmbeddings
@@ -36,30 +35,12 @@
 )
 from neo4j_graphrag.experimental.pipeline import Pipeline
 from neo4j_graphrag.experimental.pipeline.pipeline import PipelineResult
-from neo4j_graphrag.llm import OpenAILLM
-
-# set log level to DEBUG for all neo4j_graphrag.* loggers
-logging.config.dictConfig(
-    {
-        "version": 1,
-        "handlers": {
-            "console": {
-                "class": "logging.StreamHandler",
-            }
-        },
-        "loggers": {
-            "root": {
-                "handlers": ["console"],
-            },
-            "neo4j_graphrag": {
-                "level": "DEBUG",
-            },
-        },
-    }
-)
+from neo4j_graphrag.llm import LLMInterface, OpenAILLM
 
 
-async def main(neo4j_driver: neo4j.Driver) -> PipelineResult:
+async def define_and_run_pipeline(
+    neo4j_driver: neo4j.AsyncDriver, llm: LLMInterface
+) -> PipelineResult:
     """This is where we define and run the KG builder pipeline, instantiating a few
     components:
     - Text Splitter: in this example we use the fixed size text splitter
@@ -83,13 +64,7 @@ async def main(neo4j_driver: neo4j.Driver) -> PipelineResult:
     pipe.add_component(SchemaBuilder(), "schema")
     pipe.add_component(
         LLMEntityRelationExtractor(
-            llm=OpenAILLM(
-                model_name="gpt-4o",
-                model_params={
-                    "max_tokens": 1000,
-                    "response_format": {"type": "json_object"},
-                },
-            ),
+            llm=llm,
             on_error=OnError.RAISE,
         ),
         "extractor",
@@ -164,8 +139,23 @@ async def main(neo4j_driver: neo4j.Driver) -> PipelineResult:
     return await pipe.run(pipe_inputs)
 
 
-if __name__ == "__main__":
-    with neo4j.GraphDatabase.driver(
+async def main() -> PipelineResult:
+    llm = OpenAILLM(
+        model_name="gpt-4o",
+        model_params={
+            "max_tokens": 1000,
+            "response_format": {"type": "json_object"},
+        },
+    )
+    driver = neo4j.AsyncGraphDatabase.driver(
         "bolt://localhost:7687", auth=("neo4j", "password")
-    ) as driver:
-        print(asyncio.run(main(driver)))
+    )
+    res = await define_and_run_pipeline(driver, llm)
+    await driver.close()
+    await llm.async_client.close()
+    return res
+
+
+if __name__ == "__main__":
+    res = asyncio.run(main())
+    print(res)
diff --git a/examples/pipeline/kg_builder_two_documents_entity_resolution.py b/examples/pipeline/kg_builder_two_documents_entity_resolution.py
new file mode 100644
index 00000000..eb68b022
--- /dev/null
+++ b/examples/pipeline/kg_builder_two_documents_entity_resolution.py
@@ -0,0 +1,156 @@
+#  Copyright (c) "Neo4j"
+#  Neo4j Sweden AB [https://neo4j.com]
+#  #
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#  #
+#      https://www.apache.org/licenses/LICENSE-2.0
+#  #
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+from __future__ import annotations
+
+import asyncio
+
+import neo4j
+from neo4j_graphrag.experimental.components.entity_relation_extractor import (
+    LLMEntityRelationExtractor,
+    OnError,
+)
+from neo4j_graphrag.experimental.components.kg_writer import Neo4jWriter
+from neo4j_graphrag.experimental.components.pdf_loader import PdfLoader
+from neo4j_graphrag.experimental.components.resolver import (
+    SinglePropertyExactMatchResolver,
+)
+from neo4j_graphrag.experimental.components.schema import (
+    SchemaBuilder,
+    SchemaEntity,
+    SchemaProperty,
+    SchemaRelation,
+)
+from neo4j_graphrag.experimental.components.text_splitters.fixed_size_splitter import (
+    FixedSizeSplitter,
+)
+from neo4j_graphrag.experimental.pipeline import Pipeline
+from neo4j_graphrag.llm import LLMInterface, OpenAILLM
+
+
+async def define_and_run_pipeline(
+    neo4j_driver: neo4j.AsyncDriver, llm: LLMInterface
+) -> None:
+    """This is where we define and run the KG builder pipeline, instantiating a few
+    components:
+    - Text Splitter: in this example we use the fixed size text splitter
+    - Schema Builder: this component takes a list of entities, relationships and
+        possible triplets as inputs, validate them and return a schema ready to use
+        for the rest of the pipeline
+    - LLM Entity Relation Extractor is an LLM-based entity and relation extractor:
+        based on the provided schema, the LLM will do its best to identity these
+        entities and their relations within the provided text
+    - KG writer: once entities and relations are extracted, they can be writen
+        to a Neo4j database
+    """
+    pipe = Pipeline()
+    # define the components
+    pipe.add_component(PdfLoader(), "loader")
+    pipe.add_component(
+        FixedSizeSplitter(),
+        "splitter",
+    )
+    pipe.add_component(SchemaBuilder(), "schema")
+    pipe.add_component(
+        LLMEntityRelationExtractor(
+            llm=llm,
+            on_error=OnError.IGNORE,
+        ),
+        "extractor",
+    )
+    pipe.add_component(Neo4jWriter(neo4j_driver), "writer")
+    pipe.add_component(SinglePropertyExactMatchResolver(neo4j_driver), "resolver")
+    # define the execution order of component
+    # and how the output of previous components must be used
+    pipe.connect("loader", "splitter", {"text": "loader.text"})
+    pipe.connect("splitter", "extractor", input_config={"chunks": "splitter"})
+    pipe.connect(
+        "schema",
+        "extractor",
+        input_config={"schema": "schema", "document_info": "loader.document_info"},
+    )
+    pipe.connect(
+        "extractor",
+        "writer",
+        input_config={"graph": "extractor"},
+    )
+    pipe.connect("writer", "resolver", {})
+    # user input:
+    # the initial text
+    # and the list of entities and relations we are looking for
+    pipe_inputs = {
+        "loader": {},
+        "schema": {
+            "entities": [
+                SchemaEntity(
+                    label="Person",
+                    properties=[
+                        SchemaProperty(name="name", type="STRING"),
+                        SchemaProperty(name="place_of_birth", type="STRING"),
+                        SchemaProperty(name="date_of_birth", type="DATE"),
+                    ],
+                ),
+                SchemaEntity(
+                    label="Organization",
+                    properties=[
+                        SchemaProperty(name="name", type="STRING"),
+                        SchemaProperty(name="country", type="STRING"),
+                    ],
+                ),
+            ],
+            "relations": [
+                SchemaRelation(
+                    label="WORKED_FOR",
+                ),
+                SchemaRelation(
+                    label="FRIEND",
+                ),
+                SchemaRelation(
+                    label="ENEMY",
+                ),
+            ],
+            "potential_schema": [
+                ("Person", "WORKED_FOR", "Organization"),
+                ("Person", "FRIEND", "Person"),
+                ("Person", "ENEMY", "Person"),
+            ],
+        },
+    }
+    # run the pipeline for each documents
+    for document in [
+        "examples/pipeline/Harry Potter and the Chamber of Secrets Summary.pdf",
+        "examples/pipeline/Harry Potter and the Death Hallows Summary.pdf",
+    ]:
+        pipe_inputs["loader"]["filepath"] = document
+        await pipe.run(pipe_inputs)
+
+
+async def main() -> None:
+    llm = OpenAILLM(
+        model_name="gpt-4o",
+        model_params={
+            "max_tokens": 1000,
+            "response_format": {"type": "json_object"},
+        },
+    )
+    driver = neo4j.AsyncGraphDatabase.driver(
+        "bolt://localhost:7687", auth=("neo4j", "password")
+    )
+    await define_and_run_pipeline(driver, llm)
+    await driver.close()
+    await llm.async_client.close()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/poetry.lock b/poetry.lock
index e5dbe5dc..e2e030c8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,13 +2,13 @@
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.2"
+version = "2.4.3"
 description = "Happy Eyeballs for asyncio"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "aiohappyeyeballs-2.4.2-py3-none-any.whl", hash = "sha256:8522691d9a154ba1145b157d6d5c15e5c692527ce6a53c5e5f9876977f6dab2f"},
-    {file = "aiohappyeyeballs-2.4.2.tar.gz", hash = "sha256:4ca893e6c5c1f5bf3888b04cb5a3bee24995398efef6e0b9f747b5e89d84fd74"},
+    {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"},
+    {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"},
 ]
 
 [[package]]
@@ -159,9 +159,6 @@ files = [
     {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
 ]
 
-[package.dependencies]
-typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
-
 [[package]]
 name = "anthropic"
 version = "0.34.2"
@@ -189,13 +186,13 @@ vertex = ["google-auth (>=2,<3)"]
 
 [[package]]
 name = "anyio"
-version = "4.5.0"
+version = "4.6.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.5.0-py3-none-any.whl", hash = "sha256:fdeb095b7cc5a5563175eedd926ec4ae55413bb4be5770c424af0ba46ccb4a78"},
-    {file = "anyio-4.5.0.tar.gz", hash = "sha256:c5a275fe5ca0afd788001f58fca1e69e29ce706d746e317d660e21f70c530ef9"},
+    {file = "anyio-4.6.0-py3-none-any.whl", hash = "sha256:c7d2e9d63e31599eeb636c8c5c03a7e108d73b345f064f1c19fdc87b79036a9a"},
+    {file = "anyio-4.6.0.tar.gz", hash = "sha256:137b4559cbb034c477165047febb6ff83f390fc3b20bf181c1fc0a728cb8beeb"},
 ]
 
 [package.dependencies]
@@ -211,13 +208,13 @@ trio = ["trio (>=0.26.1)"]
 
 [[package]]
 name = "astroid"
-version = "3.2.4"
+version = "3.3.4"
 description = "An abstract syntax tree for Python with inference support."
 optional = false
-python-versions = ">=3.8.0"
+python-versions = ">=3.9.0"
 files = [
-    {file = "astroid-3.2.4-py3-none-any.whl", hash = "sha256:413658a61eeca6202a59231abb473f932038fbcbf1666587f66d482083413a25"},
-    {file = "astroid-3.2.4.tar.gz", hash = "sha256:0e14202810b30da1b735827f78f5157be2bbd4a7a59b7707ca0bfc2fb4c0063a"},
+    {file = "astroid-3.3.4-py3-none-any.whl", hash = "sha256:5eba185467253501b62a9f113c263524b4f5d55e1b30456370eed4cdbd6438fd"},
+    {file = "astroid-3.3.4.tar.gz", hash = "sha256:e73d0b62dd680a7c07cb2cd0ce3c22570b044dd01bd994bc3a2dd16c6cbba162"},
 ]
 
 [package.dependencies]
@@ -304,17 +301,17 @@ lxml = ["lxml"]
 
 [[package]]
 name = "boto3"
-version = "1.35.29"
+version = "1.35.30"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "boto3-1.35.29-py3-none-any.whl", hash = "sha256:2244044cdfa8ac345d7400536dc15a4824835e7ec5c55bc267e118af66bb27db"},
-    {file = "boto3-1.35.29.tar.gz", hash = "sha256:7bbb1ee649e09e956952285782cfdebd7e81fc78384f48dfab3d66c6eaf3f63f"},
+    {file = "boto3-1.35.30-py3-none-any.whl", hash = "sha256:d89c3459db89c5408e83219ab849ffd0146bc4285e75cdc67c6e45d390a12df2"},
+    {file = "boto3-1.35.30.tar.gz", hash = "sha256:d2851aec8e9dc6937977acbe9a5124ecc31b3ad5f50a10cd9ae52636da3f52fa"},
 ]
 
 [package.dependencies]
-botocore = ">=1.35.29,<1.36.0"
+botocore = ">=1.35.30,<1.36.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -323,13 +320,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.35.29"
+version = "1.35.30"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.35.29-py3-none-any.whl", hash = "sha256:f8e3ae0d84214eff3fb69cb4dc51cea6c43d3bde82027a94d00c52b941d6c3d5"},
-    {file = "botocore-1.35.29.tar.gz", hash = "sha256:4ed28ab03675bb008a290c452c5ddd7aaa5d4e3fa1912aadbdf93057ee84362b"},
+    {file = "botocore-1.35.30-py3-none-any.whl", hash = "sha256:3bb9f9dde001608671ea74681ac3cec06bbbb10cba8cb8c1387a25e843075ce0"},
+    {file = "botocore-1.35.30.tar.gz", hash = "sha256:ab5350e8a50e48d371fa2d517d65c29a40c43788cb9a15387f93eac5a23df0fd"},
 ]
 
 [package.dependencies]
@@ -1257,79 +1254,38 @@ tracing = ["opentelemetry-api (>=1.1.0)"]
 
 [[package]]
 name = "google-crc32c"
-version = "1.5.0"
+version = "1.6.0"
 description = "A python wrapper of the C library 'Google CRC32C'"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 files = [
-    {file = "google-crc32c-1.5.0.tar.gz", hash = "sha256:89284716bc6a5a415d4eaa11b1726d2d60a0cd12aadf5439828353662ede9dd7"},
-    {file = "google_crc32c-1.5.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:596d1f98fc70232fcb6590c439f43b350cb762fb5d61ce7b0e9db4539654cc13"},
-    {file = "google_crc32c-1.5.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:be82c3c8cfb15b30f36768797a640e800513793d6ae1724aaaafe5bf86f8f346"},
-    {file = "google_crc32c-1.5.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:461665ff58895f508e2866824a47bdee72497b091c730071f2b7575d5762ab65"},
-    {file = "google_crc32c-1.5.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e2096eddb4e7c7bdae4bd69ad364e55e07b8316653234a56552d9c988bd2d61b"},
-    {file = "google_crc32c-1.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:116a7c3c616dd14a3de8c64a965828b197e5f2d121fedd2f8c5585c547e87b02"},
-    {file = "google_crc32c-1.5.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:5829b792bf5822fd0a6f6eb34c5f81dd074f01d570ed7f36aa101d6fc7a0a6e4"},
-    {file = "google_crc32c-1.5.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:64e52e2b3970bd891309c113b54cf0e4384762c934d5ae56e283f9a0afcd953e"},
-    {file = "google_crc32c-1.5.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:02ebb8bf46c13e36998aeaad1de9b48f4caf545e91d14041270d9dca767b780c"},
-    {file = "google_crc32c-1.5.0-cp310-cp310-win32.whl", hash = "sha256:2e920d506ec85eb4ba50cd4228c2bec05642894d4c73c59b3a2fe20346bd00ee"},
-    {file = "google_crc32c-1.5.0-cp310-cp310-win_amd64.whl", hash = "sha256:07eb3c611ce363c51a933bf6bd7f8e3878a51d124acfc89452a75120bc436289"},
-    {file = "google_crc32c-1.5.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:cae0274952c079886567f3f4f685bcaf5708f0a23a5f5216fdab71f81a6c0273"},
-    {file = "google_crc32c-1.5.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:1034d91442ead5a95b5aaef90dbfaca8633b0247d1e41621d1e9f9db88c36298"},
-    {file = "google_crc32c-1.5.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7c42c70cd1d362284289c6273adda4c6af8039a8ae12dc451dcd61cdabb8ab57"},
-    {file = "google_crc32c-1.5.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8485b340a6a9e76c62a7dce3c98e5f102c9219f4cfbf896a00cf48caf078d438"},
-    {file = "google_crc32c-1.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:77e2fd3057c9d78e225fa0a2160f96b64a824de17840351b26825b0848022906"},
-    {file = "google_crc32c-1.5.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:f583edb943cf2e09c60441b910d6a20b4d9d626c75a36c8fcac01a6c96c01183"},
-    {file = "google_crc32c-1.5.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:a1fd716e7a01f8e717490fbe2e431d2905ab8aa598b9b12f8d10abebb36b04dd"},
-    {file = "google_crc32c-1.5.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:72218785ce41b9cfd2fc1d6a017dc1ff7acfc4c17d01053265c41a2c0cc39b8c"},
-    {file = "google_crc32c-1.5.0-cp311-cp311-win32.whl", hash = "sha256:66741ef4ee08ea0b2cc3c86916ab66b6aef03768525627fd6a1b34968b4e3709"},
-    {file = "google_crc32c-1.5.0-cp311-cp311-win_amd64.whl", hash = "sha256:ba1eb1843304b1e5537e1fca632fa894d6f6deca8d6389636ee5b4797affb968"},
-    {file = "google_crc32c-1.5.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:98cb4d057f285bd80d8778ebc4fde6b4d509ac3f331758fb1528b733215443ae"},
-    {file = "google_crc32c-1.5.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fd8536e902db7e365f49e7d9029283403974ccf29b13fc7028b97e2295b33556"},
-    {file = "google_crc32c-1.5.0-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:19e0a019d2c4dcc5e598cd4a4bc7b008546b0358bd322537c74ad47a5386884f"},
-    {file = "google_crc32c-1.5.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:02c65b9817512edc6a4ae7c7e987fea799d2e0ee40c53ec573a692bee24de876"},
-    {file = "google_crc32c-1.5.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:6ac08d24c1f16bd2bf5eca8eaf8304812f44af5cfe5062006ec676e7e1d50afc"},
-    {file = "google_crc32c-1.5.0-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:3359fc442a743e870f4588fcf5dcbc1bf929df1fad8fb9905cd94e5edb02e84c"},
-    {file = "google_crc32c-1.5.0-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:1e986b206dae4476f41bcec1faa057851f3889503a70e1bdb2378d406223994a"},
-    {file = "google_crc32c-1.5.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:de06adc872bcd8c2a4e0dc51250e9e65ef2ca91be023b9d13ebd67c2ba552e1e"},
-    {file = "google_crc32c-1.5.0-cp37-cp37m-win32.whl", hash = "sha256:d3515f198eaa2f0ed49f8819d5732d70698c3fa37384146079b3799b97667a94"},
-    {file = "google_crc32c-1.5.0-cp37-cp37m-win_amd64.whl", hash = "sha256:67b741654b851abafb7bc625b6d1cdd520a379074e64b6a128e3b688c3c04740"},
-    {file = "google_crc32c-1.5.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:c02ec1c5856179f171e032a31d6f8bf84e5a75c45c33b2e20a3de353b266ebd8"},
-    {file = "google_crc32c-1.5.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:edfedb64740750e1a3b16152620220f51d58ff1b4abceb339ca92e934775c27a"},
-    {file = "google_crc32c-1.5.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:84e6e8cd997930fc66d5bb4fde61e2b62ba19d62b7abd7a69920406f9ecca946"},
-    {file = "google_crc32c-1.5.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:024894d9d3cfbc5943f8f230e23950cd4906b2fe004c72e29b209420a1e6b05a"},
-    {file = "google_crc32c-1.5.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:998679bf62b7fb599d2878aa3ed06b9ce688b8974893e7223c60db155f26bd8d"},
-    {file = "google_crc32c-1.5.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:83c681c526a3439b5cf94f7420471705bbf96262f49a6fe546a6db5f687a3d4a"},
-    {file = "google_crc32c-1.5.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:4c6fdd4fccbec90cc8a01fc00773fcd5fa28db683c116ee3cb35cd5da9ef6c37"},
-    {file = "google_crc32c-1.5.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:5ae44e10a8e3407dbe138984f21e536583f2bba1be9491239f942c2464ac0894"},
-    {file = "google_crc32c-1.5.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:37933ec6e693e51a5b07505bd05de57eee12f3e8c32b07da7e73669398e6630a"},
-    {file = "google_crc32c-1.5.0-cp38-cp38-win32.whl", hash = "sha256:fe70e325aa68fa4b5edf7d1a4b6f691eb04bbccac0ace68e34820d283b5f80d4"},
-    {file = "google_crc32c-1.5.0-cp38-cp38-win_amd64.whl", hash = "sha256:74dea7751d98034887dbd821b7aae3e1d36eda111d6ca36c206c44478035709c"},
-    {file = "google_crc32c-1.5.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:c6c777a480337ac14f38564ac88ae82d4cd238bf293f0a22295b66eb89ffced7"},
-    {file = "google_crc32c-1.5.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:759ce4851a4bb15ecabae28f4d2e18983c244eddd767f560165563bf9aefbc8d"},
-    {file = "google_crc32c-1.5.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f13cae8cc389a440def0c8c52057f37359014ccbc9dc1f0827936bcd367c6100"},
-    {file = "google_crc32c-1.5.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e560628513ed34759456a416bf86b54b2476c59144a9138165c9a1575801d0d9"},
-    {file = "google_crc32c-1.5.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e1674e4307fa3024fc897ca774e9c7562c957af85df55efe2988ed9056dc4e57"},
-    {file = "google_crc32c-1.5.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:278d2ed7c16cfc075c91378c4f47924c0625f5fc84b2d50d921b18b7975bd210"},
-    {file = "google_crc32c-1.5.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:d5280312b9af0976231f9e317c20e4a61cd2f9629b7bfea6a693d1878a264ebd"},
-    {file = "google_crc32c-1.5.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:8b87e1a59c38f275c0e3676fc2ab6d59eccecfd460be267ac360cc31f7bcde96"},
-    {file = "google_crc32c-1.5.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:7c074fece789b5034b9b1404a1f8208fc2d4c6ce9decdd16e8220c5a793e6f61"},
-    {file = "google_crc32c-1.5.0-cp39-cp39-win32.whl", hash = "sha256:7f57f14606cd1dd0f0de396e1e53824c371e9544a822648cd76c034d209b559c"},
-    {file = "google_crc32c-1.5.0-cp39-cp39-win_amd64.whl", hash = "sha256:a2355cba1f4ad8b6988a4ca3feed5bff33f6af2d7f134852cf279c2aebfde541"},
-    {file = "google_crc32c-1.5.0-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:f314013e7dcd5cf45ab1945d92e713eec788166262ae8deb2cfacd53def27325"},
-    {file = "google_crc32c-1.5.0-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3b747a674c20a67343cb61d43fdd9207ce5da6a99f629c6e2541aa0e89215bcd"},
-    {file = "google_crc32c-1.5.0-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8f24ed114432de109aa9fd317278518a5af2d31ac2ea6b952b2f7782b43da091"},
-    {file = "google_crc32c-1.5.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b8667b48e7a7ef66afba2c81e1094ef526388d35b873966d8a9a447974ed9178"},
-    {file = "google_crc32c-1.5.0-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:1c7abdac90433b09bad6c43a43af253e688c9cfc1c86d332aed13f9a7c7f65e2"},
-    {file = "google_crc32c-1.5.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:6f998db4e71b645350b9ac28a2167e6632c239963ca9da411523bb439c5c514d"},
-    {file = "google_crc32c-1.5.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9c99616c853bb585301df6de07ca2cadad344fd1ada6d62bb30aec05219c45d2"},
-    {file = "google_crc32c-1.5.0-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2ad40e31093a4af319dadf503b2467ccdc8f67c72e4bcba97f8c10cb078207b5"},
-    {file = "google_crc32c-1.5.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cd67cf24a553339d5062eff51013780a00d6f97a39ca062781d06b3a73b15462"},
-    {file = "google_crc32c-1.5.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:398af5e3ba9cf768787eef45c803ff9614cc3e22a5b2f7d7ae116df8b11e3314"},
-    {file = "google_crc32c-1.5.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:b1f8133c9a275df5613a451e73f36c2aea4fe13c5c8997e22cf355ebd7bd0728"},
-    {file = "google_crc32c-1.5.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9ba053c5f50430a3fcfd36f75aff9caeba0440b2d076afdb79a318d6ca245f88"},
-    {file = "google_crc32c-1.5.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:272d3892a1e1a2dbc39cc5cde96834c236d5327e2122d3aaa19f6614531bb6eb"},
-    {file = "google_crc32c-1.5.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:635f5d4dd18758a1fbd1049a8e8d2fee4ffed124462d837d1a02a0e009c3ab31"},
-    {file = "google_crc32c-1.5.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:c672d99a345849301784604bfeaeba4db0c7aae50b95be04dd651fd2a7310b93"},
+    {file = "google_crc32c-1.6.0-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:5bcc90b34df28a4b38653c36bb5ada35671ad105c99cfe915fb5bed7ad6924aa"},
+    {file = "google_crc32c-1.6.0-cp310-cp310-macosx_12_0_x86_64.whl", hash = "sha256:d9e9913f7bd69e093b81da4535ce27af842e7bf371cde42d1ae9e9bd382dc0e9"},
+    {file = "google_crc32c-1.6.0-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:a184243544811e4a50d345838a883733461e67578959ac59964e43cca2c791e7"},
+    {file = "google_crc32c-1.6.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:236c87a46cdf06384f614e9092b82c05f81bd34b80248021f729396a78e55d7e"},
+    {file = "google_crc32c-1.6.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ebab974b1687509e5c973b5c4b8b146683e101e102e17a86bd196ecaa4d099fc"},
+    {file = "google_crc32c-1.6.0-cp310-cp310-win_amd64.whl", hash = "sha256:50cf2a96da226dcbff8671233ecf37bf6e95de98b2a2ebadbfdf455e6d05df42"},
+    {file = "google_crc32c-1.6.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:f7a1fc29803712f80879b0806cb83ab24ce62fc8daf0569f2204a0cfd7f68ed4"},
+    {file = "google_crc32c-1.6.0-cp311-cp311-macosx_12_0_x86_64.whl", hash = "sha256:40b05ab32a5067525670880eb5d169529089a26fe35dce8891127aeddc1950e8"},
+    {file = "google_crc32c-1.6.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a9e4b426c3702f3cd23b933436487eb34e01e00327fac20c9aebb68ccf34117d"},
+    {file = "google_crc32c-1.6.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:51c4f54dd8c6dfeb58d1df5e4f7f97df8abf17a36626a217f169893d1d7f3e9f"},
+    {file = "google_crc32c-1.6.0-cp311-cp311-win_amd64.whl", hash = "sha256:bb8b3c75bd157010459b15222c3fd30577042a7060e29d42dabce449c087f2b3"},
+    {file = "google_crc32c-1.6.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:ed767bf4ba90104c1216b68111613f0d5926fb3780660ea1198fc469af410e9d"},
+    {file = "google_crc32c-1.6.0-cp312-cp312-macosx_12_0_x86_64.whl", hash = "sha256:62f6d4a29fea082ac4a3c9be5e415218255cf11684ac6ef5488eea0c9132689b"},
+    {file = "google_crc32c-1.6.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c87d98c7c4a69066fd31701c4e10d178a648c2cac3452e62c6b24dc51f9fcc00"},
+    {file = "google_crc32c-1.6.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bd5e7d2445d1a958c266bfa5d04c39932dc54093fa391736dbfdb0f1929c1fb3"},
+    {file = "google_crc32c-1.6.0-cp312-cp312-win_amd64.whl", hash = "sha256:7aec8e88a3583515f9e0957fe4f5f6d8d4997e36d0f61624e70469771584c760"},
+    {file = "google_crc32c-1.6.0-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:e2806553238cd076f0a55bddab37a532b53580e699ed8e5606d0de1f856b5205"},
+    {file = "google_crc32c-1.6.0-cp39-cp39-macosx_12_0_x86_64.whl", hash = "sha256:bb0966e1c50d0ef5bc743312cc730b533491d60585a9a08f897274e57c3f70e0"},
+    {file = "google_crc32c-1.6.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:386122eeaaa76951a8196310432c5b0ef3b53590ef4c317ec7588ec554fec5d2"},
+    {file = "google_crc32c-1.6.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d2952396dc604544ea7476b33fe87faedc24d666fb0c2d5ac971a2b9576ab871"},
+    {file = "google_crc32c-1.6.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:35834855408429cecf495cac67ccbab802de269e948e27478b1e47dfb6465e57"},
+    {file = "google_crc32c-1.6.0-cp39-cp39-win_amd64.whl", hash = "sha256:d8797406499f28b5ef791f339594b0b5fdedf54e203b5066675c406ba69d705c"},
+    {file = "google_crc32c-1.6.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:48abd62ca76a2cbe034542ed1b6aee851b6f28aaca4e6551b5599b6f3ef175cc"},
+    {file = "google_crc32c-1.6.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:18e311c64008f1f1379158158bb3f0c8d72635b9eb4f9545f8cf990c5668e59d"},
+    {file = "google_crc32c-1.6.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:05e2d8c9a2f853ff116db9706b4a27350587f341eda835f46db3c0a8c8ce2f24"},
+    {file = "google_crc32c-1.6.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:91ca8145b060679ec9176e6de4f89b07363d6805bd4760631ef254905503598d"},
+    {file = "google_crc32c-1.6.0.tar.gz", hash = "sha256:6eceb6ad197656a1ff49ebfbbfa870678c75be4344feb35ac1edf694309413dc"},
 ]
 
 [package.extras]
@@ -2735,21 +2691,21 @@ files = [
 
 [[package]]
 name = "networkx"
-version = "3.1"
+version = "3.2.1"
 description = "Python package for creating and manipulating graphs and networks"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "networkx-3.1-py3-none-any.whl", hash = "sha256:4f33f68cb2afcf86f28a45f43efc27a9386b535d567d2127f8f61d51dec58d36"},
-    {file = "networkx-3.1.tar.gz", hash = "sha256:de346335408f84de0eada6ff9fafafff9bcda11f0a0dfaa931133debb146ab61"},
+    {file = "networkx-3.2.1-py3-none-any.whl", hash = "sha256:f18c69adc97877c42332c170849c96cefa91881c99a7cb3e95b7c659ebdc1ec2"},
+    {file = "networkx-3.2.1.tar.gz", hash = "sha256:9f1bb5cf3409bf324e0a722c20bdb4c20ee39bf1c30ce8ae499c8502b0b5e0c6"},
 ]
 
 [package.extras]
-default = ["matplotlib (>=3.4)", "numpy (>=1.20)", "pandas (>=1.3)", "scipy (>=1.8)"]
-developer = ["mypy (>=1.1)", "pre-commit (>=3.2)"]
-doc = ["nb2plots (>=0.6)", "numpydoc (>=1.5)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.13)", "sphinx (>=6.1)", "sphinx-gallery (>=0.12)", "texext (>=0.6.7)"]
-extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.10)", "sympy (>=1.10)"]
-test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"]
+default = ["matplotlib (>=3.5)", "numpy (>=1.22)", "pandas (>=1.4)", "scipy (>=1.9,!=1.11.0,!=1.11.1)"]
+developer = ["changelist (==0.4)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
+doc = ["nb2plots (>=0.7)", "nbconvert (<7.9)", "numpydoc (>=1.6)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.14)", "sphinx (>=7)", "sphinx-gallery (>=0.14)", "texext (>=0.6.7)"]
+extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.11)", "sympy (>=1.10)"]
+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
 name = "nltk"
@@ -2787,43 +2743,6 @@ files = [
     {file = "nodeenv-1.9.1.tar.gz", hash = "sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f"},
 ]
 
-[[package]]
-name = "numpy"
-version = "1.24.4"
-description = "Fundamental package for array computing in Python"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "numpy-1.24.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c0bfb52d2169d58c1cdb8cc1f16989101639b34c7d3ce60ed70b19c63eba0b64"},
-    {file = "numpy-1.24.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ed094d4f0c177b1b8e7aa9cba7d6ceed51c0e569a5318ac0ca9a090680a6a1b1"},
-    {file = "numpy-1.24.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:79fc682a374c4a8ed08b331bef9c5f582585d1048fa6d80bc6c35bc384eee9b4"},
-    {file = "numpy-1.24.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7ffe43c74893dbf38c2b0a1f5428760a1a9c98285553c89e12d70a96a7f3a4d6"},
-    {file = "numpy-1.24.4-cp310-cp310-win32.whl", hash = "sha256:4c21decb6ea94057331e111a5bed9a79d335658c27ce2adb580fb4d54f2ad9bc"},
-    {file = "numpy-1.24.4-cp310-cp310-win_amd64.whl", hash = "sha256:b4bea75e47d9586d31e892a7401f76e909712a0fd510f58f5337bea9572c571e"},
-    {file = "numpy-1.24.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f136bab9c2cfd8da131132c2cf6cc27331dd6fae65f95f69dcd4ae3c3639c810"},
-    {file = "numpy-1.24.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e2926dac25b313635e4d6cf4dc4e51c8c0ebfed60b801c799ffc4c32bf3d1254"},
-    {file = "numpy-1.24.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:222e40d0e2548690405b0b3c7b21d1169117391c2e82c378467ef9ab4c8f0da7"},
-    {file = "numpy-1.24.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7215847ce88a85ce39baf9e89070cb860c98fdddacbaa6c0da3ffb31b3350bd5"},
-    {file = "numpy-1.24.4-cp311-cp311-win32.whl", hash = "sha256:4979217d7de511a8d57f4b4b5b2b965f707768440c17cb70fbf254c4b225238d"},
-    {file = "numpy-1.24.4-cp311-cp311-win_amd64.whl", hash = "sha256:b7b1fc9864d7d39e28f41d089bfd6353cb5f27ecd9905348c24187a768c79694"},
-    {file = "numpy-1.24.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1452241c290f3e2a312c137a9999cdbf63f78864d63c79039bda65ee86943f61"},
-    {file = "numpy-1.24.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:04640dab83f7c6c85abf9cd729c5b65f1ebd0ccf9de90b270cd61935eef0197f"},
-    {file = "numpy-1.24.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5425b114831d1e77e4b5d812b69d11d962e104095a5b9c3b641a218abcc050e"},
-    {file = "numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd80e219fd4c71fc3699fc1dadac5dcf4fd882bfc6f7ec53d30fa197b8ee22dc"},
-    {file = "numpy-1.24.4-cp38-cp38-win32.whl", hash = "sha256:4602244f345453db537be5314d3983dbf5834a9701b7723ec28923e2889e0bb2"},
-    {file = "numpy-1.24.4-cp38-cp38-win_amd64.whl", hash = "sha256:692f2e0f55794943c5bfff12b3f56f99af76f902fc47487bdfe97856de51a706"},
-    {file = "numpy-1.24.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:2541312fbf09977f3b3ad449c4e5f4bb55d0dbf79226d7724211acc905049400"},
-    {file = "numpy-1.24.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9667575fb6d13c95f1b36aca12c5ee3356bf001b714fc354eb5465ce1609e62f"},
-    {file = "numpy-1.24.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f3a86ed21e4f87050382c7bc96571755193c4c1392490744ac73d660e8f564a9"},
-    {file = "numpy-1.24.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d11efb4dbecbdf22508d55e48d9c8384db795e1b7b51ea735289ff96613ff74d"},
-    {file = "numpy-1.24.4-cp39-cp39-win32.whl", hash = "sha256:6620c0acd41dbcb368610bb2f4d83145674040025e5536954782467100aa8835"},
-    {file = "numpy-1.24.4-cp39-cp39-win_amd64.whl", hash = "sha256:befe2bf740fd8373cf56149a5c23a0f601e82869598d41f8e188a0e9869926f8"},
-    {file = "numpy-1.24.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:31f13e25b4e304632a4619d0e0777662c2ffea99fcae2029556b17d8ff958aef"},
-    {file = "numpy-1.24.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95f7ac6540e95bc440ad77f56e520da5bf877f87dca58bd095288dce8940532a"},
-    {file = "numpy-1.24.4-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:e98f220aa76ca2a977fe435f5b04d7b3470c0a2e6312907b37ba6068f26787f2"},
-    {file = "numpy-1.24.4.tar.gz", hash = "sha256:80f5e3a4e498641401868df4208b74581206afbee7cf7b8329daae82676d9463"},
-]
-
 [[package]]
 name = "numpy"
 version = "1.26.4"
@@ -3116,70 +3035,89 @@ files = [
 
 [[package]]
 name = "pandas"
-version = "2.0.3"
+version = "2.2.3"
 description = "Powerful data structures for data analysis, time series, and statistics"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "pandas-2.0.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e4c7c9f27a4185304c7caf96dc7d91bc60bc162221152de697c98eb0b2648dd8"},
-    {file = "pandas-2.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f167beed68918d62bffb6ec64f2e1d8a7d297a038f86d4aed056b9493fca407f"},
-    {file = "pandas-2.0.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ce0c6f76a0f1ba361551f3e6dceaff06bde7514a374aa43e33b588ec10420183"},
-    {file = "pandas-2.0.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba619e410a21d8c387a1ea6e8a0e49bb42216474436245718d7f2e88a2f8d7c0"},
-    {file = "pandas-2.0.3-cp310-cp310-win32.whl", hash = "sha256:3ef285093b4fe5058eefd756100a367f27029913760773c8bf1d2d8bebe5d210"},
-    {file = "pandas-2.0.3-cp310-cp310-win_amd64.whl", hash = "sha256:9ee1a69328d5c36c98d8e74db06f4ad518a1840e8ccb94a4ba86920986bb617e"},
-    {file = "pandas-2.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b084b91d8d66ab19f5bb3256cbd5ea661848338301940e17f4492b2ce0801fe8"},
-    {file = "pandas-2.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:37673e3bdf1551b95bf5d4ce372b37770f9529743d2498032439371fc7b7eb26"},
-    {file = "pandas-2.0.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b9cb1e14fdb546396b7e1b923ffaeeac24e4cedd14266c3497216dd4448e4f2d"},
-    {file = "pandas-2.0.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d9cd88488cceb7635aebb84809d087468eb33551097d600c6dad13602029c2df"},
-    {file = "pandas-2.0.3-cp311-cp311-win32.whl", hash = "sha256:694888a81198786f0e164ee3a581df7d505024fbb1f15202fc7db88a71d84ebd"},
-    {file = "pandas-2.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:6a21ab5c89dcbd57f78d0ae16630b090eec626360085a4148693def5452d8a6b"},
-    {file = "pandas-2.0.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:9e4da0d45e7f34c069fe4d522359df7d23badf83abc1d1cef398895822d11061"},
-    {file = "pandas-2.0.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:32fca2ee1b0d93dd71d979726b12b61faa06aeb93cf77468776287f41ff8fdc5"},
-    {file = "pandas-2.0.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:258d3624b3ae734490e4d63c430256e716f488c4fcb7c8e9bde2d3aa46c29089"},
-    {file = "pandas-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9eae3dc34fa1aa7772dd3fc60270d13ced7346fcbcfee017d3132ec625e23bb0"},
-    {file = "pandas-2.0.3-cp38-cp38-win32.whl", hash = "sha256:f3421a7afb1a43f7e38e82e844e2bca9a6d793d66c1a7f9f0ff39a795bbc5e02"},
-    {file = "pandas-2.0.3-cp38-cp38-win_amd64.whl", hash = "sha256:69d7f3884c95da3a31ef82b7618af5710dba95bb885ffab339aad925c3e8ce78"},
-    {file = "pandas-2.0.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5247fb1ba347c1261cbbf0fcfba4a3121fbb4029d95d9ef4dc45406620b25c8b"},
-    {file = "pandas-2.0.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:81af086f4543c9d8bb128328b5d32e9986e0c84d3ee673a2ac6fb57fd14f755e"},
-    {file = "pandas-2.0.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1994c789bf12a7c5098277fb43836ce090f1073858c10f9220998ac74f37c69b"},
-    {file = "pandas-2.0.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ec591c48e29226bcbb316e0c1e9423622bc7a4eaf1ef7c3c9fa1a3981f89641"},
-    {file = "pandas-2.0.3-cp39-cp39-win32.whl", hash = "sha256:04dbdbaf2e4d46ca8da896e1805bc04eb85caa9a82e259e8eed00254d5e0c682"},
-    {file = "pandas-2.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:1168574b036cd8b93abc746171c9b4f1b83467438a5e45909fed645cf8692dbc"},
-    {file = "pandas-2.0.3.tar.gz", hash = "sha256:c02f372a88e0d17f36d3093a644c73cfc1788e876a7c4bcb4020a77512e2043c"},
+    {file = "pandas-2.2.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1948ddde24197a0f7add2bdc4ca83bf2b1ef84a1bc8ccffd95eda17fd836ecb5"},
+    {file = "pandas-2.2.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:381175499d3802cde0eabbaf6324cce0c4f5d52ca6f8c377c29ad442f50f6348"},
+    {file = "pandas-2.2.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d9c45366def9a3dd85a6454c0e7908f2b3b8e9c138f5dc38fed7ce720d8453ed"},
+    {file = "pandas-2.2.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:86976a1c5b25ae3f8ccae3a5306e443569ee3c3faf444dfd0f41cda24667ad57"},
+    {file = "pandas-2.2.3-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:b8661b0238a69d7aafe156b7fa86c44b881387509653fdf857bebc5e4008ad42"},
+    {file = "pandas-2.2.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:37e0aced3e8f539eccf2e099f65cdb9c8aa85109b0be6e93e2baff94264bdc6f"},
+    {file = "pandas-2.2.3-cp310-cp310-win_amd64.whl", hash = "sha256:56534ce0746a58afaf7942ba4863e0ef81c9c50d3f0ae93e9497d6a41a057645"},
+    {file = "pandas-2.2.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:66108071e1b935240e74525006034333f98bcdb87ea116de573a6a0dccb6c039"},
+    {file = "pandas-2.2.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7c2875855b0ff77b2a64a0365e24455d9990730d6431b9e0ee18ad8acee13dbd"},
+    {file = "pandas-2.2.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cd8d0c3be0515c12fed0bdbae072551c8b54b7192c7b1fda0ba56059a0179698"},
+    {file = "pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c124333816c3a9b03fbeef3a9f230ba9a737e9e5bb4060aa2107a86cc0a497fc"},
+    {file = "pandas-2.2.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:63cc132e40a2e084cf01adf0775b15ac515ba905d7dcca47e9a251819c575ef3"},
+    {file = "pandas-2.2.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:29401dbfa9ad77319367d36940cd8a0b3a11aba16063e39632d98b0e931ddf32"},
+    {file = "pandas-2.2.3-cp311-cp311-win_amd64.whl", hash = "sha256:3fc6873a41186404dad67245896a6e440baacc92f5b716ccd1bc9ed2995ab2c5"},
+    {file = "pandas-2.2.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b1d432e8d08679a40e2a6d8b2f9770a5c21793a6f9f47fdd52c5ce1948a5a8a9"},
+    {file = "pandas-2.2.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a5a1595fe639f5988ba6a8e5bc9649af3baf26df3998a0abe56c02609392e0a4"},
+    {file = "pandas-2.2.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:5de54125a92bb4d1c051c0659e6fcb75256bf799a732a87184e5ea503965bce3"},
+    {file = "pandas-2.2.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fffb8ae78d8af97f849404f21411c95062db1496aeb3e56f146f0355c9989319"},
+    {file = "pandas-2.2.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:6dfcb5ee8d4d50c06a51c2fffa6cff6272098ad6540aed1a76d15fb9318194d8"},
+    {file = "pandas-2.2.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:062309c1b9ea12a50e8ce661145c6aab431b1e99530d3cd60640e255778bd43a"},
+    {file = "pandas-2.2.3-cp312-cp312-win_amd64.whl", hash = "sha256:59ef3764d0fe818125a5097d2ae867ca3fa64df032331b7e0917cf5d7bf66b13"},
+    {file = "pandas-2.2.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f00d1345d84d8c86a63e476bb4955e46458b304b9575dcf71102b5c705320015"},
+    {file = "pandas-2.2.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:3508d914817e153ad359d7e069d752cdd736a247c322d932eb89e6bc84217f28"},
+    {file = "pandas-2.2.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:22a9d949bfc9a502d320aa04e5d02feab689d61da4e7764b62c30b991c42c5f0"},
+    {file = "pandas-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3a255b2c19987fbbe62a9dfd6cff7ff2aa9ccab3fc75218fd4b7530f01efa24"},
+    {file = "pandas-2.2.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:800250ecdadb6d9c78eae4990da62743b857b470883fa27f652db8bdde7f6659"},
+    {file = "pandas-2.2.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6374c452ff3ec675a8f46fd9ab25c4ad0ba590b71cf0656f8b6daa5202bca3fb"},
+    {file = "pandas-2.2.3-cp313-cp313-win_amd64.whl", hash = "sha256:61c5ad4043f791b61dd4752191d9f07f0ae412515d59ba8f005832a532f8736d"},
+    {file = "pandas-2.2.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:3b71f27954685ee685317063bf13c7709a7ba74fc996b84fc6821c59b0f06468"},
+    {file = "pandas-2.2.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:38cf8125c40dae9d5acc10fa66af8ea6fdf760b2714ee482ca691fc66e6fcb18"},
+    {file = "pandas-2.2.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ba96630bc17c875161df3818780af30e43be9b166ce51c9a18c1feae342906c2"},
+    {file = "pandas-2.2.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1db71525a1538b30142094edb9adc10be3f3e176748cd7acc2240c2f2e5aa3a4"},
+    {file = "pandas-2.2.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:15c0e1e02e93116177d29ff83e8b1619c93ddc9c49083f237d4312337a61165d"},
+    {file = "pandas-2.2.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:ad5b65698ab28ed8d7f18790a0dc58005c7629f227be9ecc1072aa74c0c1d43a"},
+    {file = "pandas-2.2.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:bc6b93f9b966093cb0fd62ff1a7e4c09e6d546ad7c1de191767baffc57628f39"},
+    {file = "pandas-2.2.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:5dbca4c1acd72e8eeef4753eeca07de9b1db4f398669d5994086f788a5d7cc30"},
+    {file = "pandas-2.2.3-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:8cd6d7cc958a3910f934ea8dbdf17b2364827bb4dafc38ce6eef6bb3d65ff09c"},
+    {file = "pandas-2.2.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:99df71520d25fade9db7c1076ac94eb994f4d2673ef2aa2e86ee039b6746d20c"},
+    {file = "pandas-2.2.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:31d0ced62d4ea3e231a9f228366919a5ea0b07440d9d4dac345376fd8e1477ea"},
+    {file = "pandas-2.2.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:7eee9e7cea6adf3e3d24e304ac6b8300646e2a5d1cd3a3c2abed9101b0846761"},
+    {file = "pandas-2.2.3-cp39-cp39-win_amd64.whl", hash = "sha256:4850ba03528b6dd51d6c5d273c46f183f39a9baf3f0143e566b89450965b105e"},
+    {file = "pandas-2.2.3.tar.gz", hash = "sha256:4f18ba62b61d7e192368b84517265a99b4d7ee8912f8708660fb4a366cc82667"},
 ]
 
 [package.dependencies]
 numpy = [
-    {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
-    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
-    {version = ">=1.20.3", markers = "python_version < \"3.10\""},
+    {version = ">=1.22.4", markers = "python_version < \"3.11\""},
+    {version = ">=1.23.2", markers = "python_version == \"3.11\""},
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
-tzdata = ">=2022.1"
+tzdata = ">=2022.7"
 
 [package.extras]
-all = ["PyQt5 (>=5.15.1)", "SQLAlchemy (>=1.4.16)", "beautifulsoup4 (>=4.9.3)", "bottleneck (>=1.3.2)", "brotlipy (>=0.7.0)", "fastparquet (>=0.6.3)", "fsspec (>=2021.07.0)", "gcsfs (>=2021.07.0)", "html5lib (>=1.1)", "hypothesis (>=6.34.2)", "jinja2 (>=3.0.0)", "lxml (>=4.6.3)", "matplotlib (>=3.6.1)", "numba (>=0.53.1)", "numexpr (>=2.7.3)", "odfpy (>=1.4.1)", "openpyxl (>=3.0.7)", "pandas-gbq (>=0.15.0)", "psycopg2 (>=2.8.6)", "pyarrow (>=7.0.0)", "pymysql (>=1.0.2)", "pyreadstat (>=1.1.2)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)", "python-snappy (>=0.6.0)", "pyxlsb (>=1.0.8)", "qtpy (>=2.2.0)", "s3fs (>=2021.08.0)", "scipy (>=1.7.1)", "tables (>=3.6.1)", "tabulate (>=0.8.9)", "xarray (>=0.21.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=1.4.3)", "zstandard (>=0.15.2)"]
-aws = ["s3fs (>=2021.08.0)"]
-clipboard = ["PyQt5 (>=5.15.1)", "qtpy (>=2.2.0)"]
-compression = ["brotlipy (>=0.7.0)", "python-snappy (>=0.6.0)", "zstandard (>=0.15.2)"]
-computation = ["scipy (>=1.7.1)", "xarray (>=0.21.0)"]
-excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.0.7)", "pyxlsb (>=1.0.8)", "xlrd (>=2.0.1)", "xlsxwriter (>=1.4.3)"]
-feather = ["pyarrow (>=7.0.0)"]
-fss = ["fsspec (>=2021.07.0)"]
-gcp = ["gcsfs (>=2021.07.0)", "pandas-gbq (>=0.15.0)"]
-hdf5 = ["tables (>=3.6.1)"]
-html = ["beautifulsoup4 (>=4.9.3)", "html5lib (>=1.1)", "lxml (>=4.6.3)"]
-mysql = ["SQLAlchemy (>=1.4.16)", "pymysql (>=1.0.2)"]
-output-formatting = ["jinja2 (>=3.0.0)", "tabulate (>=0.8.9)"]
-parquet = ["pyarrow (>=7.0.0)"]
-performance = ["bottleneck (>=1.3.2)", "numba (>=0.53.1)", "numexpr (>=2.7.1)"]
-plot = ["matplotlib (>=3.6.1)"]
-postgresql = ["SQLAlchemy (>=1.4.16)", "psycopg2 (>=2.8.6)"]
-spss = ["pyreadstat (>=1.1.2)"]
-sql-other = ["SQLAlchemy (>=1.4.16)"]
-test = ["hypothesis (>=6.34.2)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
-xml = ["lxml (>=4.6.3)"]
+all = ["PyQt5 (>=5.15.9)", "SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)", "beautifulsoup4 (>=4.11.2)", "bottleneck (>=1.3.6)", "dataframe-api-compat (>=0.1.7)", "fastparquet (>=2022.12.0)", "fsspec (>=2022.11.0)", "gcsfs (>=2022.11.0)", "html5lib (>=1.1)", "hypothesis (>=6.46.1)", "jinja2 (>=3.1.2)", "lxml (>=4.9.2)", "matplotlib (>=3.6.3)", "numba (>=0.56.4)", "numexpr (>=2.8.4)", "odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "pandas-gbq (>=0.19.0)", "psycopg2 (>=2.9.6)", "pyarrow (>=10.0.1)", "pymysql (>=1.0.2)", "pyreadstat (>=1.2.0)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "qtpy (>=2.3.0)", "s3fs (>=2022.11.0)", "scipy (>=1.10.0)", "tables (>=3.8.0)", "tabulate (>=0.9.0)", "xarray (>=2022.12.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)", "zstandard (>=0.19.0)"]
+aws = ["s3fs (>=2022.11.0)"]
+clipboard = ["PyQt5 (>=5.15.9)", "qtpy (>=2.3.0)"]
+compression = ["zstandard (>=0.19.0)"]
+computation = ["scipy (>=1.10.0)", "xarray (>=2022.12.0)"]
+consortium-standard = ["dataframe-api-compat (>=0.1.7)"]
+excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)"]
+feather = ["pyarrow (>=10.0.1)"]
+fss = ["fsspec (>=2022.11.0)"]
+gcp = ["gcsfs (>=2022.11.0)", "pandas-gbq (>=0.19.0)"]
+hdf5 = ["tables (>=3.8.0)"]
+html = ["beautifulsoup4 (>=4.11.2)", "html5lib (>=1.1)", "lxml (>=4.9.2)"]
+mysql = ["SQLAlchemy (>=2.0.0)", "pymysql (>=1.0.2)"]
+output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.9.0)"]
+parquet = ["pyarrow (>=10.0.1)"]
+performance = ["bottleneck (>=1.3.6)", "numba (>=0.56.4)", "numexpr (>=2.8.4)"]
+plot = ["matplotlib (>=3.6.3)"]
+postgresql = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "psycopg2 (>=2.9.6)"]
+pyarrow = ["pyarrow (>=10.0.1)"]
+spss = ["pyreadstat (>=1.2.0)"]
+sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)"]
+test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
+xml = ["lxml (>=4.9.2)"]
 
 [[package]]
 name = "parameterized"
@@ -3609,17 +3547,17 @@ files = [
 
 [[package]]
 name = "pylint"
-version = "3.2.7"
+version = "3.3.1"
 description = "python code static checker"
 optional = false
-python-versions = ">=3.8.0"
+python-versions = ">=3.9.0"
 files = [
-    {file = "pylint-3.2.7-py3-none-any.whl", hash = "sha256:02f4aedeac91be69fb3b4bea997ce580a4ac68ce58b89eaefeaf06749df73f4b"},
-    {file = "pylint-3.2.7.tar.gz", hash = "sha256:1b7a721b575eaeaa7d39db076b6e7743c993ea44f57979127c517c6c572c803e"},
+    {file = "pylint-3.3.1-py3-none-any.whl", hash = "sha256:2f846a466dd023513240bc140ad2dd73bfc080a5d85a710afdb728c420a5a2b9"},
+    {file = "pylint-3.3.1.tar.gz", hash = "sha256:9f3dcc87b1203e612b78d91a896407787e708b3f189b5fa0b307712d49ff0c6e"},
 ]
 
 [package.dependencies]
-astroid = ">=3.2.4,<=3.3.0-dev0"
+astroid = ">=3.3.4,<=3.4.0-dev0"
 colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
 dill = [
     {version = ">=0.2", markers = "python_version < \"3.11\""},
@@ -4136,88 +4074,90 @@ torch = ["safetensors[numpy]", "torch (>=1.10)"]
 
 [[package]]
 name = "scikit-learn"
-version = "1.3.2"
+version = "1.5.2"
 description = "A set of python modules for machine learning and data mining"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "scikit-learn-1.3.2.tar.gz", hash = "sha256:a2f54c76accc15a34bfb9066e6c7a56c1e7235dda5762b990792330b52ccfb05"},
-    {file = "scikit_learn-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e326c0eb5cf4d6ba40f93776a20e9a7a69524c4db0757e7ce24ba222471ee8a1"},
-    {file = "scikit_learn-1.3.2-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:535805c2a01ccb40ca4ab7d081d771aea67e535153e35a1fd99418fcedd1648a"},
-    {file = "scikit_learn-1.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1215e5e58e9880b554b01187b8c9390bf4dc4692eedeaf542d3273f4785e342c"},
-    {file = "scikit_learn-1.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0ee107923a623b9f517754ea2f69ea3b62fc898a3641766cb7deb2f2ce450161"},
-    {file = "scikit_learn-1.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:35a22e8015048c628ad099da9df5ab3004cdbf81edc75b396fd0cff8699ac58c"},
-    {file = "scikit_learn-1.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6fb6bc98f234fda43163ddbe36df8bcde1d13ee176c6dc9b92bb7d3fc842eb66"},
-    {file = "scikit_learn-1.3.2-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:18424efee518a1cde7b0b53a422cde2f6625197de6af36da0b57ec502f126157"},
-    {file = "scikit_learn-1.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3271552a5eb16f208a6f7f617b8cc6d1f137b52c8a1ef8edf547db0259b2c9fb"},
-    {file = "scikit_learn-1.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc4144a5004a676d5022b798d9e573b05139e77f271253a4703eed295bde0433"},
-    {file = "scikit_learn-1.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:67f37d708f042a9b8d59551cf94d30431e01374e00dc2645fa186059c6c5d78b"},
-    {file = "scikit_learn-1.3.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:8db94cd8a2e038b37a80a04df8783e09caac77cbe052146432e67800e430c028"},
-    {file = "scikit_learn-1.3.2-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:61a6efd384258789aa89415a410dcdb39a50e19d3d8410bd29be365bcdd512d5"},
-    {file = "scikit_learn-1.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cb06f8dce3f5ddc5dee1715a9b9f19f20d295bed8e3cd4fa51e1d050347de525"},
-    {file = "scikit_learn-1.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5b2de18d86f630d68fe1f87af690d451388bb186480afc719e5f770590c2ef6c"},
-    {file = "scikit_learn-1.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:0402638c9a7c219ee52c94cbebc8fcb5eb9fe9c773717965c1f4185588ad3107"},
-    {file = "scikit_learn-1.3.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:a19f90f95ba93c1a7f7924906d0576a84da7f3b2282ac3bfb7a08a32801add93"},
-    {file = "scikit_learn-1.3.2-cp38-cp38-macosx_12_0_arm64.whl", hash = "sha256:b8692e395a03a60cd927125eef3a8e3424d86dde9b2370d544f0ea35f78a8073"},
-    {file = "scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:15e1e94cc23d04d39da797ee34236ce2375ddea158b10bee3c343647d615581d"},
-    {file = "scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:785a2213086b7b1abf037aeadbbd6d67159feb3e30263434139c98425e3dcfcf"},
-    {file = "scikit_learn-1.3.2-cp38-cp38-win_amd64.whl", hash = "sha256:64381066f8aa63c2710e6b56edc9f0894cc7bf59bd71b8ce5613a4559b6145e0"},
-    {file = "scikit_learn-1.3.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:6c43290337f7a4b969d207e620658372ba3c1ffb611f8bc2b6f031dc5c6d1d03"},
-    {file = "scikit_learn-1.3.2-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:dc9002fc200bed597d5d34e90c752b74df516d592db162f756cc52836b38fe0e"},
-    {file = "scikit_learn-1.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1d08ada33e955c54355d909b9c06a4789a729977f165b8bae6f225ff0a60ec4a"},
-    {file = "scikit_learn-1.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:763f0ae4b79b0ff9cca0bf3716bcc9915bdacff3cebea15ec79652d1cc4fa5c9"},
-    {file = "scikit_learn-1.3.2-cp39-cp39-win_amd64.whl", hash = "sha256:ed932ea780517b00dae7431e031faae6b49b20eb6950918eb83bd043237950e0"},
+    {file = "scikit_learn-1.5.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:299406827fb9a4f862626d0fe6c122f5f87f8910b86fe5daa4c32dcd742139b6"},
+    {file = "scikit_learn-1.5.2-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:2d4cad1119c77930b235579ad0dc25e65c917e756fe80cab96aa3b9428bd3fb0"},
+    {file = "scikit_learn-1.5.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8c412ccc2ad9bf3755915e3908e677b367ebc8d010acbb3f182814524f2e5540"},
+    {file = "scikit_learn-1.5.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3a686885a4b3818d9e62904d91b57fa757fc2bed3e465c8b177be652f4dd37c8"},
+    {file = "scikit_learn-1.5.2-cp310-cp310-win_amd64.whl", hash = "sha256:c15b1ca23d7c5f33cc2cb0a0d6aaacf893792271cddff0edbd6a40e8319bc113"},
+    {file = "scikit_learn-1.5.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:03b6158efa3faaf1feea3faa884c840ebd61b6484167c711548fce208ea09445"},
+    {file = "scikit_learn-1.5.2-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:1ff45e26928d3b4eb767a8f14a9a6efbf1cbff7c05d1fb0f95f211a89fd4f5de"},
+    {file = "scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f763897fe92d0e903aa4847b0aec0e68cadfff77e8a0687cabd946c89d17e675"},
+    {file = "scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f8b0ccd4a902836493e026c03256e8b206656f91fbcc4fde28c57a5b752561f1"},
+    {file = "scikit_learn-1.5.2-cp311-cp311-win_amd64.whl", hash = "sha256:6c16d84a0d45e4894832b3c4d0bf73050939e21b99b01b6fd59cbb0cf39163b6"},
+    {file = "scikit_learn-1.5.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f932a02c3f4956dfb981391ab24bda1dbd90fe3d628e4b42caef3e041c67707a"},
+    {file = "scikit_learn-1.5.2-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:3b923d119d65b7bd555c73be5423bf06c0105678ce7e1f558cb4b40b0a5502b1"},
+    {file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f60021ec1574e56632be2a36b946f8143bf4e5e6af4a06d85281adc22938e0dd"},
+    {file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:394397841449853c2290a32050382edaec3da89e35b3e03d6cc966aebc6a8ae6"},
+    {file = "scikit_learn-1.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:57cc1786cfd6bd118220a92ede80270132aa353647684efa385a74244a41e3b1"},
+    {file = "scikit_learn-1.5.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:757c7d514ddb00ae249832fe87100d9c73c6ea91423802872d9e74970a0e40b9"},
+    {file = "scikit_learn-1.5.2-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:52788f48b5d8bca5c0736c175fa6bdaab2ef00a8f536cda698db61bd89c551c1"},
+    {file = "scikit_learn-1.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:643964678f4b5fbdc95cbf8aec638acc7aa70f5f79ee2cdad1eec3df4ba6ead8"},
+    {file = "scikit_learn-1.5.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ca64b3089a6d9b9363cd3546f8978229dcbb737aceb2c12144ee3f70f95684b7"},
+    {file = "scikit_learn-1.5.2-cp39-cp39-win_amd64.whl", hash = "sha256:3bed4909ba187aca80580fe2ef370d9180dcf18e621a27c4cf2ef10d279a7efe"},
+    {file = "scikit_learn-1.5.2.tar.gz", hash = "sha256:b4237ed7b3fdd0a4882792e68ef2545d5baa50aca3bb45aa7df468138ad8f94d"},
 ]
 
 [package.dependencies]
-joblib = ">=1.1.1"
-numpy = ">=1.17.3,<2.0"
-scipy = ">=1.5.0"
-threadpoolctl = ">=2.0.0"
+joblib = ">=1.2.0"
+numpy = ">=1.19.5"
+scipy = ">=1.6.0"
+threadpoolctl = ">=3.1.0"
 
 [package.extras]
-benchmark = ["matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)", "pandas (>=1.0.5)"]
-docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)", "sphinx (>=6.0.0)", "sphinx-copybutton (>=0.5.2)", "sphinx-gallery (>=0.10.1)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"]
-examples = ["matplotlib (>=3.1.3)", "pandas (>=1.0.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)"]
-tests = ["black (>=23.3.0)", "matplotlib (>=3.1.3)", "mypy (>=1.3)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.0.272)", "scikit-image (>=0.16.2)"]
+benchmark = ["matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "pandas (>=1.1.5)"]
+build = ["cython (>=3.0.10)", "meson-python (>=0.16.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)"]
+docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "polars (>=0.20.30)", "pooch (>=1.6.0)", "pydata-sphinx-theme (>=0.15.3)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)", "sphinx (>=7.3.7)", "sphinx-copybutton (>=0.5.2)", "sphinx-design (>=0.5.0)", "sphinx-design (>=0.6.0)", "sphinx-gallery (>=0.16.0)", "sphinx-prompt (>=1.4.0)", "sphinx-remove-toctrees (>=1.0.0.post1)", "sphinxcontrib-sass (>=0.3.4)", "sphinxext-opengraph (>=0.9.1)"]
+examples = ["matplotlib (>=3.3.4)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)"]
+install = ["joblib (>=1.2.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)", "threadpoolctl (>=3.1.0)"]
+maintenance = ["conda-lock (==2.5.6)"]
+tests = ["black (>=24.3.0)", "matplotlib (>=3.3.4)", "mypy (>=1.9)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "polars (>=0.20.30)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pyarrow (>=12.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.2.1)", "scikit-image (>=0.17.2)"]
 
 [[package]]
 name = "scipy"
-version = "1.10.1"
+version = "1.13.1"
 description = "Fundamental algorithms for scientific computing in Python"
 optional = false
-python-versions = "<3.12,>=3.8"
-files = [
-    {file = "scipy-1.10.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e7354fd7527a4b0377ce55f286805b34e8c54b91be865bac273f527e1b839019"},
-    {file = "scipy-1.10.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:4b3f429188c66603a1a5c549fb414e4d3bdc2a24792e061ffbd607d3d75fd84e"},
-    {file = "scipy-1.10.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1553b5dcddd64ba9a0d95355e63fe6c3fc303a8fd77c7bc91e77d61363f7433f"},
-    {file = "scipy-1.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4c0ff64b06b10e35215abce517252b375e580a6125fd5fdf6421b98efbefb2d2"},
-    {file = "scipy-1.10.1-cp310-cp310-win_amd64.whl", hash = "sha256:fae8a7b898c42dffe3f7361c40d5952b6bf32d10c4569098d276b4c547905ee1"},
-    {file = "scipy-1.10.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0f1564ea217e82c1bbe75ddf7285ba0709ecd503f048cb1236ae9995f64217bd"},
-    {file = "scipy-1.10.1-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:d925fa1c81b772882aa55bcc10bf88324dadb66ff85d548c71515f6689c6dac5"},
-    {file = "scipy-1.10.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aaea0a6be54462ec027de54fca511540980d1e9eea68b2d5c1dbfe084797be35"},
-    {file = "scipy-1.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:15a35c4242ec5f292c3dd364a7c71a61be87a3d4ddcc693372813c0b73c9af1d"},
-    {file = "scipy-1.10.1-cp311-cp311-win_amd64.whl", hash = "sha256:43b8e0bcb877faf0abfb613d51026cd5cc78918e9530e375727bf0625c82788f"},
-    {file = "scipy-1.10.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:5678f88c68ea866ed9ebe3a989091088553ba12c6090244fdae3e467b1139c35"},
-    {file = "scipy-1.10.1-cp38-cp38-macosx_12_0_arm64.whl", hash = "sha256:39becb03541f9e58243f4197584286e339029e8908c46f7221abeea4b749fa88"},
-    {file = "scipy-1.10.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bce5869c8d68cf383ce240e44c1d9ae7c06078a9396df68ce88a1230f93a30c1"},
-    {file = "scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:07c3457ce0b3ad5124f98a86533106b643dd811dd61b548e78cf4c8786652f6f"},
-    {file = "scipy-1.10.1-cp38-cp38-win_amd64.whl", hash = "sha256:049a8bbf0ad95277ffba9b3b7d23e5369cc39e66406d60422c8cfef40ccc8415"},
-    {file = "scipy-1.10.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:cd9f1027ff30d90618914a64ca9b1a77a431159df0e2a195d8a9e8a04c78abf9"},
-    {file = "scipy-1.10.1-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:79c8e5a6c6ffaf3a2262ef1be1e108a035cf4f05c14df56057b64acc5bebffb6"},
-    {file = "scipy-1.10.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:51af417a000d2dbe1ec6c372dfe688e041a7084da4fdd350aeb139bd3fb55353"},
-    {file = "scipy-1.10.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1b4735d6c28aad3cdcf52117e0e91d6b39acd4272f3f5cd9907c24ee931ad601"},
-    {file = "scipy-1.10.1-cp39-cp39-win_amd64.whl", hash = "sha256:7ff7f37b1bf4417baca958d254e8e2875d0cc23aaadbe65b3d5b3077b0eb23ea"},
-    {file = "scipy-1.10.1.tar.gz", hash = "sha256:2cf9dfb80a7b4589ba4c40ce7588986d6d5cebc5457cad2c2880f6bc2d42f3a5"},
+python-versions = ">=3.9"
+files = [
+    {file = "scipy-1.13.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:20335853b85e9a49ff7572ab453794298bcf0354d8068c5f6775a0eabf350aca"},
+    {file = "scipy-1.13.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:d605e9c23906d1994f55ace80e0125c587f96c020037ea6aa98d01b4bd2e222f"},
+    {file = "scipy-1.13.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cfa31f1def5c819b19ecc3a8b52d28ffdcc7ed52bb20c9a7589669dd3c250989"},
+    {file = "scipy-1.13.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f26264b282b9da0952a024ae34710c2aff7d27480ee91a2e82b7b7073c24722f"},
+    {file = "scipy-1.13.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:eccfa1906eacc02de42d70ef4aecea45415f5be17e72b61bafcfd329bdc52e94"},
+    {file = "scipy-1.13.1-cp310-cp310-win_amd64.whl", hash = "sha256:2831f0dc9c5ea9edd6e51e6e769b655f08ec6db6e2e10f86ef39bd32eb11da54"},
+    {file = "scipy-1.13.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:27e52b09c0d3a1d5b63e1105f24177e544a222b43611aaf5bc44d4a0979e32f9"},
+    {file = "scipy-1.13.1-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:54f430b00f0133e2224c3ba42b805bfd0086fe488835effa33fa291561932326"},
+    {file = "scipy-1.13.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e89369d27f9e7b0884ae559a3a956e77c02114cc60a6058b4e5011572eea9299"},
+    {file = "scipy-1.13.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a78b4b3345f1b6f68a763c6e25c0c9a23a9fd0f39f5f3d200efe8feda560a5fa"},
+    {file = "scipy-1.13.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:45484bee6d65633752c490404513b9ef02475b4284c4cfab0ef946def50b3f59"},
+    {file = "scipy-1.13.1-cp311-cp311-win_amd64.whl", hash = "sha256:5713f62f781eebd8d597eb3f88b8bf9274e79eeabf63afb4a737abc6c84ad37b"},
+    {file = "scipy-1.13.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:5d72782f39716b2b3509cd7c33cdc08c96f2f4d2b06d51e52fb45a19ca0c86a1"},
+    {file = "scipy-1.13.1-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:017367484ce5498445aade74b1d5ab377acdc65e27095155e448c88497755a5d"},
+    {file = "scipy-1.13.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:949ae67db5fa78a86e8fa644b9a6b07252f449dcf74247108c50e1d20d2b4627"},
+    {file = "scipy-1.13.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:de3ade0e53bc1f21358aa74ff4830235d716211d7d077e340c7349bc3542e884"},
+    {file = "scipy-1.13.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:2ac65fb503dad64218c228e2dc2d0a0193f7904747db43014645ae139c8fad16"},
+    {file = "scipy-1.13.1-cp312-cp312-win_amd64.whl", hash = "sha256:cdd7dacfb95fea358916410ec61bbc20440f7860333aee6d882bb8046264e949"},
+    {file = "scipy-1.13.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:436bbb42a94a8aeef855d755ce5a465479c721e9d684de76bf61a62e7c2b81d5"},
+    {file = "scipy-1.13.1-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:8335549ebbca860c52bf3d02f80784e91a004b71b059e3eea9678ba994796a24"},
+    {file = "scipy-1.13.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d533654b7d221a6a97304ab63c41c96473ff04459e404b83275b60aa8f4b7004"},
+    {file = "scipy-1.13.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:637e98dcf185ba7f8e663e122ebf908c4702420477ae52a04f9908707456ba4d"},
+    {file = "scipy-1.13.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:a014c2b3697bde71724244f63de2476925596c24285c7a637364761f8710891c"},
+    {file = "scipy-1.13.1-cp39-cp39-win_amd64.whl", hash = "sha256:392e4ec766654852c25ebad4f64e4e584cf19820b980bc04960bca0b0cd6eaa2"},
+    {file = "scipy-1.13.1.tar.gz", hash = "sha256:095a87a0312b08dfd6a6155cbbd310a8c51800fc931b8c0b84003014b874ed3c"},
 ]
 
 [package.dependencies]
-numpy = ">=1.19.5,<1.27.0"
+numpy = ">=1.22.4,<2.3"
 
 [package.extras]
-dev = ["click", "doit (>=0.36.0)", "flake8", "mypy", "pycodestyle", "pydevtool", "rich-click", "typing_extensions"]
-doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
-test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
+doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.12.0)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
+test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "scipy"
@@ -4933,30 +4873,31 @@ optree = ["optree (>=0.11.0)"]
 
 [[package]]
 name = "tox"
-version = "4.20.0"
+version = "4.21.0"
 description = "tox is a generic virtualenv management and test command line tool"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tox-4.20.0-py3-none-any.whl", hash = "sha256:21a8005e3d3fe5658a8e36b8ca3ed13a4230429063c5cc2a2fdac6ee5aa0de34"},
-    {file = "tox-4.20.0.tar.gz", hash = "sha256:5b78a49b6eaaeab3ae4186415e7c97d524f762ae967c63562687c3e5f0ec23d5"},
+    {file = "tox-4.21.0-py3-none-any.whl", hash = "sha256:693ac51378255d34ad7aab6dd2ce9ab6a1cf1924eb930183fde850ad503b681d"},
+    {file = "tox-4.21.0.tar.gz", hash = "sha256:e64dd9847ff3a7ec90368be412d7efe61a39caf043222ffbe9ad638ea435f6f6"},
 ]
 
 [package.dependencies]
 cachetools = ">=5.5"
 chardet = ">=5.2"
 colorama = ">=0.4.6"
-filelock = ">=3.15.4"
+filelock = ">=3.16.1"
 packaging = ">=24.1"
-platformdirs = ">=4.2.2"
+platformdirs = ">=4.3.6"
 pluggy = ">=1.5"
-pyproject-api = ">=1.7.1"
+pyproject-api = ">=1.8"
 tomli = {version = ">=2.0.1", markers = "python_version < \"3.11\""}
-virtualenv = ">=20.26.3"
+typing-extensions = {version = ">=4.12.2", markers = "python_version < \"3.11\""}
+virtualenv = ">=20.26.6"
 
 [package.extras]
-docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-argparse-cli (>=1.17)", "sphinx-autodoc-typehints (>=2.4)", "sphinx-copybutton (>=0.5.2)", "sphinx-inline-tabs (>=2023.4.21)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=24.8)"]
-testing = ["build[virtualenv] (>=1.2.2)", "covdefaults (>=2.3)", "detect-test-pollution (>=1.2)", "devpi-process (>=1)", "diff-cover (>=9.1.1)", "distlib (>=0.3.8)", "flaky (>=3.8.1)", "hatch-vcs (>=0.4)", "hatchling (>=1.25)", "psutil (>=6)", "pytest (>=8.3.2)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-xdist (>=3.6.1)", "re-assert (>=1.1)", "setuptools (>=74.1.2)", "time-machine (>=2.15)", "wheel (>=0.44)"]
+docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-argparse-cli (>=1.18.2)", "sphinx-autodoc-typehints (>=2.4.4)", "sphinx-copybutton (>=0.5.2)", "sphinx-inline-tabs (>=2023.4.21)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=24.8)"]
+testing = ["build[virtualenv] (>=1.2.2)", "covdefaults (>=2.3)", "detect-test-pollution (>=1.2)", "devpi-process (>=1.0.2)", "diff-cover (>=9.2)", "distlib (>=0.3.8)", "flaky (>=3.8.1)", "hatch-vcs (>=0.4)", "hatchling (>=1.25)", "psutil (>=6)", "pytest (>=8.3.3)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-xdist (>=3.6.1)", "re-assert (>=1.1)", "setuptools (>=75.1)", "time-machine (>=2.15)", "wheel (>=0.44)"]
 
 [[package]]
 name = "tqdm"
@@ -5453,5 +5394,5 @@ kg-creation-tools = ["pygraphviz", "pygraphviz"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = "^3.8.1"
-content-hash = "660e13e30d6c69da33af3f771daf387d5af1c5c29c1c44de3b22d1354aca97b5"
+python-versions = "^3.9.0"
+content-hash = "ecb73179848945e83c04273f59675703c9874be1db45ed9b810890c37b12dbfb"
diff --git a/pyproject.toml b/pyproject.toml
index b06b30a4..3aacbea8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,7 +15,7 @@
 
 [tool.poetry]
 name = "neo4j-graphrag"
-version = "0.6.3"
+version = "0.7.0"
 description = "Python package to allow easy integration to Neo4j's GraphRAG features"
 authors = ["Neo4j, Inc <team-gen-ai@neo4j.com>"]
 license = "Apache License, Version 2.0"
@@ -28,7 +28,7 @@ include = "neo4j_graphrag"
 from = "src"
 
 [tool.poetry.dependencies]
-python = "^3.8.1"
+python = "^3.9.0"
 neo4j = "^5.17.0"
 pydantic = "^2.6.3"
 weaviate-client = {version = "^4.6.1", optional = true}
diff --git a/src/neo4j_graphrag/experimental/components/kg_writer.py b/src/neo4j_graphrag/experimental/components/kg_writer.py
index c0424c72..dcae6d91 100644
--- a/src/neo4j_graphrag/experimental/components/kg_writer.py
+++ b/src/neo4j_graphrag/experimental/components/kg_writer.py
@@ -18,11 +18,15 @@
 import inspect
 import logging
 from abc import abstractmethod
-from typing import Any, Dict, Literal, Optional, Tuple
+from typing import Any, Generator, Literal, Optional
 
 import neo4j
 from pydantic import validate_call
 
+from neo4j_graphrag.experimental.components.entity_relation_extractor import (
+    CHUNK_NODE_LABEL,
+    DOCUMENT_NODE_LABEL,
+)
 from neo4j_graphrag.experimental.components.types import (
     Neo4jGraph,
     Neo4jNode,
@@ -34,14 +38,25 @@
 logger = logging.getLogger(__name__)
 
 
+def batched(rows: list[Any], batch_size: int) -> Generator[list[Any], None, None]:
+    index = 0
+    for i in range(0, len(rows), batch_size):
+        start = i
+        end = min(start + batch_size, len(rows))
+        batch = rows[start:end]
+        yield batch
+        index += 1
+
+
 class KGWriterModel(DataModel):
     """Data model for the output of the Knowledge Graph writer.
 
     Attributes:
-        status (Literal["SUCCESS", "FAILURE"]): Whether or not the write operation was successful.
+        status (Literal["SUCCESS", "FAILURE"]): Whether the write operation was successful.
     """
 
     status: Literal["SUCCESS", "FAILURE"]
+    metadata: Optional[dict[str, Any]] = None
 
 
 class KGWriter(Component):
@@ -91,90 +106,85 @@ def __init__(
         self,
         driver: neo4j.driver,
         neo4j_database: Optional[str] = None,
+        batch_size: int = 1000,
         max_concurrency: int = 5,
     ):
         self.driver = driver
         self.neo4j_database = neo4j_database
+        self.batch_size = batch_size
         self.max_concurrency = max_concurrency
 
     def _db_setup(self) -> None:
         # create index on __Entity__.id
+        # used when creating the relationships
         self.driver.execute_query(
-            "CREATE INDEX __entity__id IF NOT EXISTS  FOR (n:__Entity__) ON (n.id)"
+            "CREATE INDEX __entity__id IF NOT EXISTS  FOR (n:__KGBuilder__) ON (n.id)"
         )
 
     async def _async_db_setup(self) -> None:
         # create index on __Entity__.id
+        # used when creating the relationships
         await self.driver.execute_query(
-            "CREATE INDEX __entity__id IF NOT EXISTS  FOR (n:__Entity__) ON (n.id)"
+            "CREATE INDEX __entity__id IF NOT EXISTS  FOR (n:__KGBuilder__) ON (n.id)"
         )
 
-    def _get_node_query(self, node: Neo4jNode) -> Tuple[str, Dict[str, Any]]:
-        # Create the initial node
-        parameters = {
-            "id": node.id,
-            "properties": node.properties or {},
-            "embeddings": node.embedding_properties,
-        }
-        query = UPSERT_NODE_QUERY.format(label=node.label)
-        return query, parameters
-
-    def _upsert_node(self, node: Neo4jNode) -> None:
+    @staticmethod
+    def _nodes_to_rows(nodes: list[Neo4jNode]) -> list[dict[str, Any]]:
+        rows = []
+        for node in nodes:
+            labels = [node.label]
+            if node.label not in (CHUNK_NODE_LABEL, DOCUMENT_NODE_LABEL):
+                labels.append("__Entity__")
+            row = node.model_dump()
+            row["labels"] = labels
+            rows.append(row)
+        return rows
+
+    def _upsert_nodes(self, nodes: list[Neo4jNode]) -> None:
         """Upserts a single node into the Neo4j database."
 
         Args:
-            node (Neo4jNode): The node to upsert into the database.
+            nodes (list[Neo4jNode]): The nodes batch to upsert into the database.
         """
-        query, parameters = self._get_node_query(node)
-        self.driver.execute_query(query, parameters_=parameters)
+        parameters = {"rows": self._nodes_to_rows(nodes)}
+        self.driver.execute_query(UPSERT_NODE_QUERY, parameters_=parameters)
 
-    async def _async_upsert_node(
+    async def _async_upsert_nodes(
         self,
-        node: Neo4jNode,
+        nodes: list[Neo4jNode],
         sem: asyncio.Semaphore,
     ) -> None:
         """Asynchronously upserts a single node into the Neo4j database."
 
         Args:
-            node (Neo4jNode): The node to upsert into the database.
+            nodes (list[Neo4jNode]): The nodes batch to upsert into the database.
         """
         async with sem:
-            query, parameters = self._get_node_query(node)
-            await self.driver.execute_query(query, parameters_=parameters)
-
-    def _get_rel_query(self, rel: Neo4jRelationship) -> Tuple[str, Dict[str, Any]]:
-        # Create the initial relationship
-        parameters = {
-            "start_node_id": rel.start_node_id,
-            "end_node_id": rel.end_node_id,
-            "properties": rel.properties or {},
-            "embeddings": rel.embedding_properties,
-        }
-        query = UPSERT_RELATIONSHIP_QUERY.format(
-            type=rel.type,
-        )
-        return query, parameters
+            parameters = {"rows": self._nodes_to_rows(nodes)}
+            await self.driver.execute_query(UPSERT_NODE_QUERY, parameters_=parameters)
 
-    def _upsert_relationship(self, rel: Neo4jRelationship) -> None:
+    def _upsert_relationships(self, rels: list[Neo4jRelationship]) -> None:
         """Upserts a single relationship into the Neo4j database.
 
         Args:
-            rel (Neo4jRelationship): The relationship to upsert into the database.
+            rels (list[Neo4jRelationship]): The relationships batch to upsert into the database.
         """
-        query, parameters = self._get_rel_query(rel)
-        self.driver.execute_query(query, parameters_=parameters)
+        parameters = {"rows": [rel.model_dump() for rel in rels]}
+        self.driver.execute_query(UPSERT_RELATIONSHIP_QUERY, parameters_=parameters)
 
-    async def _async_upsert_relationship(
-        self, rel: Neo4jRelationship, sem: asyncio.Semaphore
+    async def _async_upsert_relationships(
+        self, rels: list[Neo4jRelationship], sem: asyncio.Semaphore
     ) -> None:
         """Asynchronously upserts a single relationship into the Neo4j database.
 
         Args:
-            rel (Neo4jRelationship): The relationship to upsert into the database.
+            rels (list[Neo4jRelationship]): The relationships batch to upsert into the database.
         """
         async with sem:
-            query, parameters = self._get_rel_query(rel)
-            await self.driver.execute_query(query, parameters_=parameters)
+            parameters = {"rows": [rel.model_dump() for rel in rels]}
+            await self.driver.execute_query(
+                UPSERT_RELATIONSHIP_QUERY, parameters_=parameters
+            )
 
     @validate_call
     async def run(self, graph: Neo4jGraph) -> KGWriterModel:
@@ -188,25 +198,32 @@ async def run(self, graph: Neo4jGraph) -> KGWriterModel:
                 await self._async_db_setup()
                 sem = asyncio.Semaphore(self.max_concurrency)
                 node_tasks = [
-                    self._async_upsert_node(node, sem) for node in graph.nodes
+                    self._async_upsert_nodes(batch, sem)
+                    for batch in batched(graph.nodes, self.batch_size)
                 ]
                 await asyncio.gather(*node_tasks)
 
                 rel_tasks = [
-                    self._async_upsert_relationship(rel, sem)
-                    for rel in graph.relationships
+                    self._async_upsert_relationships(batch, sem)
+                    for batch in batched(graph.relationships, self.batch_size)
                 ]
                 await asyncio.gather(*rel_tasks)
             else:
                 self._db_setup()
 
-                for node in graph.nodes:
-                    self._upsert_node(node)
+                for batch in batched(graph.nodes, self.batch_size):
+                    self._upsert_nodes(batch)
 
-                for rel in graph.relationships:
-                    self._upsert_relationship(rel)
+                for batch in batched(graph.relationships, self.batch_size):
+                    self._upsert_relationships(batch)
 
-            return KGWriterModel(status="SUCCESS")
+            return KGWriterModel(
+                status="SUCCESS",
+                metadata={
+                    "node_count": len(graph.nodes),
+                    "relationship_count": len(graph.relationships),
+                },
+            )
         except neo4j.exceptions.ClientError as e:
             logger.exception(e)
-            return KGWriterModel(status="FAILURE")
+            return KGWriterModel(status="FAILURE", metadata={"error": str(e)})
diff --git a/src/neo4j_graphrag/experimental/components/pdf_loader.py b/src/neo4j_graphrag/experimental/components/pdf_loader.py
index f17e3b02..2cd3fca1 100644
--- a/src/neo4j_graphrag/experimental/components/pdf_loader.py
+++ b/src/neo4j_graphrag/experimental/components/pdf_loader.py
@@ -25,7 +25,7 @@
 from fsspec.implementations.local import LocalFileSystem
 
 from neo4j_graphrag.exceptions import PdfLoaderError
-from neo4j_graphrag.experimental.pipeline import Component, DataModel
+from neo4j_graphrag.experimental.pipeline.component import Component, DataModel
 
 
 class DocumentInfo(DataModel):
diff --git a/src/neo4j_graphrag/experimental/components/resolver.py b/src/neo4j_graphrag/experimental/components/resolver.py
new file mode 100644
index 00000000..4e07a1d6
--- /dev/null
+++ b/src/neo4j_graphrag/experimental/components/resolver.py
@@ -0,0 +1,142 @@
+#  Copyright (c) "Neo4j"
+#  Neo4j Sweden AB [https://neo4j.com]
+#  #
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#  #
+#      https://www.apache.org/licenses/LICENSE-2.0
+#  #
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+import abc
+from typing import Any, Optional, Union
+
+import neo4j
+
+from neo4j_graphrag.experimental.components.types import ResolutionStats
+from neo4j_graphrag.experimental.pipeline import Component
+from neo4j_graphrag.utils import execute_query
+
+
+class EntityResolver(Component, abc.ABC):
+    """Entity resolution base class
+
+    Args:
+        driver (neo4j.driver): The Neo4j driver to connect to the database.
+        filter_query (Optional[str]): Cypher query to select the entities to resolve. By default, all nodes with __Entity__ label are used
+    """
+
+    def __init__(
+        self,
+        driver: Union[neo4j.Driver, neo4j.AsyncDriver],
+        filter_query: Optional[str] = None,
+    ) -> None:
+        self.driver = driver
+        self.filter_query = filter_query
+
+    @abc.abstractmethod
+    async def run(self, *args: Any, **kwargs: Any) -> ResolutionStats:
+        pass
+
+
+class SinglePropertyExactMatchResolver(EntityResolver):
+    """Resolve entities with same label and exact same property (default is "name").
+
+    Args:
+        driver (neo4j.driver): The Neo4j driver to connect to the database.
+        filter_query (Optional[str]): To reduce the resolution scope, add a Cypher WHERE clause.
+        resolve_property (str): The property that will be compared (default: "name"). If values match exactly, entities are merged.
+        neo4j_database (Optional[str]): The name of the Neo4j database to write to. Defaults to 'neo4j' if not provided.
+
+    Example:
+
+    .. code-block:: python
+
+        from neo4j import AsyncGraphDatabase
+        from neo4j_graphrag.experimental.components.resolver import SinglePropertyExactMatchResolver
+
+        URI = "neo4j://localhost:7687"
+        AUTH = ("neo4j", "password")
+        DATABASE = "neo4j"
+
+        driver = AsyncGraphDatabase.driver(URI, auth=AUTH, database=DATABASE)
+        resolver = SinglePropertyExactMatchResolver(driver=driver, neo4j_database=DATABASE)
+        await resolver.run()  # no expected parameters
+
+    """
+
+    def __init__(
+        self,
+        driver: Union[neo4j.Driver, neo4j.AsyncDriver],
+        filter_query: Optional[str] = None,
+        resolve_property: str = "name",
+        neo4j_database: Optional[str] = None,
+    ) -> None:
+        super().__init__(driver, filter_query)
+        self.resolve_property = resolve_property
+        self.database = neo4j_database
+
+    async def run(self) -> ResolutionStats:
+        """Resolve entities based on the following rule:
+        For each entity label, entities with the same 'resolve_property' value
+        (exact match) are grouped into a single node:
+
+        - Properties: the property from the first node will remain if already set, otherwise the first property in list will be written.
+        - Relationships: merge relationships with same type and target node.
+
+        See apoc.refactor.mergeNodes documentation for more details.
+        """
+        match_query = "MATCH (entity:__Entity__) "
+        if self.filter_query:
+            match_query += self.filter_query
+        stat_query = f"{match_query} RETURN count(entity) as c"
+        records = await execute_query(
+            self.driver,
+            stat_query,
+            database_=self.database,
+        )
+        number_of_nodes_to_resolve = records[0].get("c")
+        if number_of_nodes_to_resolve == 0:
+            return ResolutionStats(
+                number_of_nodes_to_resolve=0,
+            )
+        merge_nodes_query = (
+            f"{match_query} "
+            f"WITH entity, entity.{self.resolve_property} as prop "
+            # keep only entities for which the resolve_property (name) is not null
+            "WITH entity, prop WHERE prop IS NOT NULL "
+            # will check the property for each of the entity labels,
+            # except the reserved ones __Entity__ and __KGBuilder__
+            "UNWIND labels(entity) as lab  "
+            "WITH lab, prop, entity WHERE NOT lab IN ['__Entity__', '__KGBuilder__'] "
+            # aggregate based on property value and label
+            # collect all entities with exact same property and label
+            # in the 'entities' list
+            "WITH prop, lab, collect(entity) AS entities "
+            # merge all entities into a single node
+            # * merge relationships: if the merged entities have a relationship of same
+            # type to the same target node, these relationships are merged
+            # otherwise relationships are just attached to the newly created node
+            # * properties: if the two entities have the same property key with
+            # different values, only one of them is kept in the created node
+            "CALL apoc.refactor.mergeNodes(entities,{ "
+            " properties:'discard', "
+            " mergeRels:true "
+            "}) "
+            "YIELD node "
+            "RETURN count(node) as c "
+        )
+        records = await execute_query(
+            self.driver,
+            merge_nodes_query,
+            database_=self.database,
+        )
+        number_of_created_nodes = records[0].get("c")
+        return ResolutionStats(
+            number_of_nodes_to_resolve=number_of_nodes_to_resolve,
+            number_of_created_nodes=number_of_created_nodes,
+        )
diff --git a/src/neo4j_graphrag/experimental/components/schema.py b/src/neo4j_graphrag/experimental/components/schema.py
index 20919d8a..c82a7d5f 100644
--- a/src/neo4j_graphrag/experimental/components/schema.py
+++ b/src/neo4j_graphrag/experimental/components/schema.py
@@ -19,7 +19,7 @@
 from pydantic import BaseModel, ValidationError, model_validator, validate_call
 
 from neo4j_graphrag.exceptions import SchemaValidationError
-from neo4j_graphrag.experimental.pipeline import Component, DataModel
+from neo4j_graphrag.experimental.pipeline.component import Component, DataModel
 
 
 class SchemaProperty(BaseModel):
diff --git a/src/neo4j_graphrag/experimental/components/types.py b/src/neo4j_graphrag/experimental/components/types.py
index 1a93a321..2591c890 100644
--- a/src/neo4j_graphrag/experimental/components/types.py
+++ b/src/neo4j_graphrag/experimental/components/types.py
@@ -51,13 +51,13 @@ class Neo4jNode(BaseModel):
     Attributes:
         id (str): The ID of the node.
         label (str): The label of the node.
-        properties (Optional[dict[str, Any]]): A dictionary of properties attached to the node.
+        properties (dict[str, Any]): A dictionary of properties attached to the node.
         embedding_properties (Optional[dict[str, list[float]]]): A list of embedding properties attached to the node.
     """
 
     id: str
     label: str
-    properties: Optional[dict[str, Any]] = None
+    properties: dict[str, Any] = {}
     embedding_properties: Optional[dict[str, list[float]]] = None
 
     @field_validator("properties", "embedding_properties")
@@ -77,14 +77,14 @@ class Neo4jRelationship(BaseModel):
         start_node_id (str): The ID of the start node.
         end_node_id (str): The ID of the end node.
         type (str): The relationship type.
-        properties (Optional[dict[str, Any]]): A dictionary of properties attached to the relationship.
+        properties (dict[str, Any]): A dictionary of properties attached to the relationship.
         embedding_properties (Optional[dict[str, list[float]]]): A list of embedding properties attached to the relationship.
     """
 
     start_node_id: str
     end_node_id: str
     type: str
-    properties: Optional[dict[str, Any]] = None
+    properties: dict[str, Any] = {}
     embedding_properties: Optional[dict[str, list[float]]] = None
 
 
@@ -98,3 +98,8 @@ class Neo4jGraph(DataModel):
 
     nodes: list[Neo4jNode] = []
     relationships: list[Neo4jRelationship] = []
+
+
+class ResolutionStats(DataModel):
+    number_of_nodes_to_resolve: int
+    number_of_created_nodes: Optional[int] = None
diff --git a/src/neo4j_graphrag/experimental/pipeline/component.py b/src/neo4j_graphrag/experimental/pipeline/component.py
index b6a5963e..84cd5bc0 100644
--- a/src/neo4j_graphrag/experimental/pipeline/component.py
+++ b/src/neo4j_graphrag/experimental/pipeline/component.py
@@ -73,6 +73,7 @@ class Component(abc.ABC, metaclass=ComponentMeta):
 
     # these variables are filled by the metaclass
     # added here for the type checker
+    # DO NOT CHANGE
     component_inputs: dict[str, dict[str, str | bool]]
     component_outputs: dict[str, dict[str, str | bool]]
 
diff --git a/src/neo4j_graphrag/experimental/pipeline/kg_builder.py b/src/neo4j_graphrag/experimental/pipeline/kg_builder.py
new file mode 100644
index 00000000..2ba0c7c0
--- /dev/null
+++ b/src/neo4j_graphrag/experimental/pipeline/kg_builder.py
@@ -0,0 +1,244 @@
+#  Copyright (c) "Neo4j"
+#  Neo4j Sweden AB [https://neo4j.com]
+#  #
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#  #
+#      https://www.apache.org/licenses/LICENSE-2.0
+#  #
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+from __future__ import annotations
+
+from typing import Any, List, Optional, Union
+
+import neo4j
+from pydantic import BaseModel, ConfigDict, Field
+
+from neo4j_graphrag.experimental.components.entity_relation_extractor import (
+    LLMEntityRelationExtractor,
+    OnError,
+)
+from neo4j_graphrag.experimental.components.kg_writer import Neo4jWriter
+from neo4j_graphrag.experimental.components.pdf_loader import PdfLoader
+from neo4j_graphrag.experimental.components.resolver import (
+    SinglePropertyExactMatchResolver,
+)
+from neo4j_graphrag.experimental.components.schema import (
+    SchemaBuilder,
+    SchemaEntity,
+    SchemaRelation,
+)
+from neo4j_graphrag.experimental.components.text_splitters.fixed_size_splitter import (
+    FixedSizeSplitter,
+)
+from neo4j_graphrag.experimental.pipeline.exceptions import PipelineDefinitionError
+from neo4j_graphrag.experimental.pipeline.pipeline import Pipeline, PipelineResult
+from neo4j_graphrag.generation.prompts import ERExtractionTemplate
+from neo4j_graphrag.llm.base import LLMInterface
+
+
+class SimpleKGPipelineConfig(BaseModel):
+    llm: LLMInterface
+    driver: neo4j.Driver
+    from_pdf: bool
+    entities: list[SchemaEntity] = Field(default_factory=list)
+    relations: list[SchemaRelation] = Field(default_factory=list)
+    potential_schema: list[tuple[str, str, str]] = Field(default_factory=list)
+    pdf_loader: Any = None
+    kg_writer: Any = None
+    text_splitter: Any = None
+    on_error: OnError = OnError.RAISE
+    prompt_template: Union[ERExtractionTemplate, str] = ERExtractionTemplate()
+    perform_entity_resolution: bool = True
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+
+class SimpleKGPipeline:
+    """
+    A class to simplify the process of building a knowledge graph from text documents.
+    It abstracts away the complexity of setting up the pipeline and its components.
+
+    Args:
+        llm (LLMInterface): An instance of an LLM to use for entity and relation extraction.
+        driver (neo4j.Driver): A Neo4j driver instance for database connection.
+        entities (Optional[List[str]]): A list of entity labels as strings.
+        relations (Optional[List[str]]): A list of relation labels as strings.
+        potential_schema (Optional[List[tuple]]): A list of potential schema relationships.
+        from_pdf (bool): Determines whether to include the PdfLoader in the pipeline.
+                         If True, expects `file_path` input in `run` methods.
+                         If False, expects `text` input in `run` methods.
+        text_splitter (Optional[Any]): A text splitter component. Defaults to FixedSizeSplitter().
+        pdf_loader (Optional[Any]): A PDF loader component. Defaults to PdfLoader().
+        kg_writer (Optional[Any]): A knowledge graph writer component. Defaults to Neo4jWriter().
+        on_error (str): Error handling strategy. Defaults to "RAISE". Possible values: "RAISE" or "IGNORE".
+        perform_entity_resolution (bool): Merge entities with same label and name. Default: True
+    """
+
+    def __init__(
+        self,
+        llm: LLMInterface,
+        driver: neo4j.Driver,
+        entities: Optional[List[str]] = None,
+        relations: Optional[List[str]] = None,
+        potential_schema: Optional[List[tuple[str, str, str]]] = None,
+        from_pdf: bool = True,
+        text_splitter: Optional[Any] = None,
+        pdf_loader: Optional[Any] = None,
+        kg_writer: Optional[Any] = None,
+        on_error: str = "RAISE",
+        prompt_template: Union[ERExtractionTemplate, str] = ERExtractionTemplate(),
+        perform_entity_resolution: bool = True,
+    ):
+        self.entities = [SchemaEntity(label=label) for label in entities or []]
+        self.relations = [SchemaRelation(label=label) for label in relations or []]
+        self.potential_schema = potential_schema if potential_schema is not None else []
+
+        try:
+            on_error_enum = OnError(on_error)
+        except ValueError:
+            raise PipelineDefinitionError(
+                f"Invalid value for on_error: {on_error}. Expected 'RAISE' or 'CONTINUE'."
+            )
+
+        config = SimpleKGPipelineConfig(
+            llm=llm,
+            driver=driver,
+            entities=self.entities,
+            relations=self.relations,
+            potential_schema=self.potential_schema,
+            from_pdf=from_pdf,
+            pdf_loader=pdf_loader,
+            kg_writer=kg_writer,
+            text_splitter=text_splitter,
+            on_error=on_error_enum,
+            prompt_template=prompt_template,
+            perform_entity_resolution=perform_entity_resolution,
+        )
+
+        self.from_pdf = config.from_pdf
+        self.llm = config.llm
+        self.driver = config.driver
+        self.text_splitter = config.text_splitter or FixedSizeSplitter()
+        self.on_error = config.on_error
+        self.pdf_loader = config.pdf_loader if pdf_loader is not None else PdfLoader()
+        self.kg_writer = (
+            config.kg_writer if kg_writer is not None else Neo4jWriter(driver)
+        )
+        self.prompt_template = config.prompt_template
+        self.perform_entity_resolution = config.perform_entity_resolution
+
+        self.pipeline = self._build_pipeline()
+
+    def _build_pipeline(self) -> Pipeline:
+        pipe = Pipeline()
+
+        pipe.add_component(self.text_splitter, "splitter")
+        pipe.add_component(SchemaBuilder(), "schema")
+        pipe.add_component(
+            LLMEntityRelationExtractor(
+                llm=self.llm,
+                on_error=self.on_error,
+                prompt_template=self.prompt_template,
+            ),
+            "extractor",
+        )
+        pipe.add_component(self.kg_writer, "writer")
+
+        if self.from_pdf:
+            pipe.add_component(self.pdf_loader, "pdf_loader")
+
+            pipe.connect(
+                "pdf_loader",
+                "splitter",
+                input_config={"text": "pdf_loader.text"},
+            )
+
+            pipe.connect(
+                "schema",
+                "extractor",
+                input_config={
+                    "schema": "schema",
+                    "document_info": "pdf_loader.document_info",
+                },
+            )
+        else:
+            pipe.connect(
+                "schema",
+                "extractor",
+                input_config={
+                    "schema": "schema",
+                },
+            )
+
+        pipe.connect(
+            "splitter",
+            "extractor",
+            input_config={"chunks": "splitter"},
+        )
+
+        # Connect extractor to writer
+        pipe.connect(
+            "extractor",
+            "writer",
+            input_config={"graph": "extractor"},
+        )
+
+        if self.perform_entity_resolution:
+            pipe.add_component(
+                SinglePropertyExactMatchResolver(self.driver), "resolver"
+            )
+            pipe.connect("writer", "resolver", {})
+
+        return pipe
+
+    async def run_async(
+        self, file_path: Optional[str] = None, text: Optional[str] = None
+    ) -> PipelineResult:
+        """
+        Asynchronously runs the knowledge graph building process.
+
+        Args:
+            file_path (Optional[str]): The path to the PDF file to process. Required if `from_pdf` is True.
+            text (Optional[str]): The text content to process. Required if `from_pdf` is False.
+
+        Returns:
+            PipelineResult: The result of the pipeline execution.
+        """
+        pipe_inputs = self._prepare_inputs(file_path=file_path, text=text)
+        return await self.pipeline.run(pipe_inputs)
+
+    def _prepare_inputs(
+        self, file_path: Optional[str], text: Optional[str]
+    ) -> dict[str, Any]:
+        if self.from_pdf:
+            if file_path is None or text is not None:
+                raise PipelineDefinitionError(
+                    "Expected 'file_path' argument when 'from_pdf' is True."
+                )
+        else:
+            if text is None or file_path is not None:
+                raise PipelineDefinitionError(
+                    "Expected 'text' argument when 'from_pdf' is False."
+                )
+
+        pipe_inputs: dict[str, Any] = {
+            "schema": {
+                "entities": self.entities,
+                "relations": self.relations,
+                "potential_schema": self.potential_schema,
+            },
+        }
+
+        if self.from_pdf:
+            pipe_inputs["pdf_loader"] = {"filepath": file_path}
+        else:
+            pipe_inputs["splitter"] = {"text": text}
+
+        return pipe_inputs
diff --git a/src/neo4j_graphrag/experimental/pipeline/pipeline.py b/src/neo4j_graphrag/experimental/pipeline/pipeline.py
index c3598121..3d004eb8 100644
--- a/src/neo4j_graphrag/experimental/pipeline/pipeline.py
+++ b/src/neo4j_graphrag/experimental/pipeline/pipeline.py
@@ -195,8 +195,9 @@ async def check_dependencies_complete(self, task: TaskPipelineNode) -> None:
         for d in dependencies:
             d_status = await self.get_status_for_component(d.start)
             if d_status != RunStatus.DONE:
-                logger.warning(
-                    f"Missing dependency {d.start} for {task.name} (status: {d_status})"
+                logger.debug(
+                    f"Missing dependency {d.start} for {task.name} (status: {d_status}). "
+                    "Will try again when dependency is complete."
                 )
                 raise PipelineMissingDependencyError()
 
@@ -605,6 +606,7 @@ def validate_parameter_mapping_for_task(self, task: TaskPipelineNode) -> bool:
     async def run(self, data: dict[str, Any]) -> PipelineResult:
         logger.debug("Starting pipeline")
         start_time = default_timer()
+        self.invalidate()
         self.validate_input_data(data)
         orchestrator = Orchestrator(self)
         await orchestrator.run(data)
diff --git a/src/neo4j_graphrag/neo4j_queries.py b/src/neo4j_graphrag/neo4j_queries.py
index 144ef178..fd819e5c 100644
--- a/src/neo4j_graphrag/neo4j_queries.py
+++ b/src/neo4j_graphrag/neo4j_queries.py
@@ -42,27 +42,31 @@
 )
 
 UPSERT_NODE_QUERY = (
-    "MERGE (n:__Entity__ {{id: $id}}) "
-    "WITH n SET n:`{label}`, n += $properties "
-    "WITH n CALL {{ "
-    "WITH n WITH n WHERE $embeddings IS NOT NULL "
-    "UNWIND keys($embeddings) as emb "
-    "CALL db.create.setNodeVectorProperty(n, emb, $embeddings[emb]) "
-    "}} "
+    "UNWIND $rows AS row "
+    "CREATE (n:__KGBuilder__ {id: row.id}) "
+    "SET n += row.properties "
+    "WITH n, row CALL apoc.create.addLabels(n, row.labels) YIELD node "
+    "WITH node as n, row CALL { "
+    "WITH n, row WITH n, row WHERE row.embedding_properties IS NOT NULL "
+    "UNWIND keys(row.embedding_properties) as emb "
+    "CALL db.create.setNodeVectorProperty(n, emb, row.embedding_properties[emb]) "
+    "RETURN count(*) as nbEmb "
+    "} "
     "RETURN elementId(n)"
 )
 
 UPSERT_RELATIONSHIP_QUERY = (
-    "MATCH (start:__Entity__ {{ id: $start_node_id }}) "
-    "MATCH (end:__Entity__ {{ id: $end_node_id }}) "
-    "MERGE (start)-[r:`{type}`]->(end) "
-    "WITH r SET r += $properties "
-    "WITH r CALL {{ "
-    "WITH r WITH r WHERE $embeddings IS NOT NULL "
-    "UNWIND keys($embeddings) as emb "
-    "CALL db.create.setRelationshipVectorProperty(r, emb, $embeddings[emb]) "
-    "}} "
-    "RETURN elementId(r)"
+    "UNWIND $rows as row "
+    "MATCH (start:__KGBuilder__ {id: row.start_node_id}) "
+    "MATCH (end:__KGBuilder__ {id: row.end_node_id}) "
+    "WITH start, end, row "
+    "CALL apoc.merge.relationship(start, row.type, {}, row.properties, end, row.properties) YIELD rel  "
+    "WITH rel, row CALL { "
+    "WITH rel, row WITH rel, row WHERE row.embedding_properties IS NOT NULL "
+    "UNWIND keys(row.embedding_properties) as emb "
+    "CALL db.create.setRelationshipVectorProperty(rel, emb, row.embedding_properties[emb]) "
+    "} "
+    "RETURN elementId(rel)"
 )
 
 UPSERT_VECTOR_ON_NODE_QUERY = (
diff --git a/src/neo4j_graphrag/utils.py b/src/neo4j_graphrag/utils.py
index e86f7588..5f1b322f 100644
--- a/src/neo4j_graphrag/utils.py
+++ b/src/neo4j_graphrag/utils.py
@@ -14,7 +14,10 @@
 #  limitations under the License.
 from __future__ import annotations
 
-from typing import Optional
+import inspect
+from typing import Any, Optional, Union
+
+import neo4j
 
 
 def validate_search_query_input(
@@ -22,3 +25,15 @@ def validate_search_query_input(
 ) -> None:
     if not (bool(query_vector) ^ bool(query_text)):
         raise ValueError("You must provide exactly one of query_vector or query_text.")
+
+
+async def execute_query(
+    driver: Union[neo4j.Driver, neo4j.AsyncDriver], query: str, **kwargs: Any
+) -> list[neo4j.Record]:
+    if inspect.iscoroutinefunction(driver.execute_query):
+        records, _, _ = await driver.execute_query(query, **kwargs)
+        return records  # type: ignore[no-any-return]
+    # ignoring type because mypy complains about coroutine
+    # but we're sure at this stage we do not have a coroutine anymore
+    records, _, _ = driver.execute_query(query, **kwargs)  # type: ignore[misc]
+    return records  # type: ignore[no-any-return]
diff --git a/tests/e2e/data/documents/harry_potter_part1.txt b/tests/e2e/data/documents/harry_potter_part1.txt
new file mode 100644
index 00000000..0b6a6621
--- /dev/null
+++ b/tests/e2e/data/documents/harry_potter_part1.txt
@@ -0,0 +1,8 @@
+At Malfoy Manor, Snape tells Voldemort the date that Harry’s friends are planning to
+move him from the house on Privet Drive to a new safe location, so that Voldemort
+can capture Harry en route.
+
+As Harry packs to leave Privet Drive, he reads two obituaries for Dumbledore, both
+of which make him think that he didn’t know Dumbledore as well as he should have.
+Downstairs, he bids good-bye to the Dursleys for the final time, as the threat of
+Voldemort forces them to go into hiding themselves.
diff --git a/tests/e2e/data/documents/harry_potter_part2.txt b/tests/e2e/data/documents/harry_potter_part2.txt
new file mode 100644
index 00000000..8a5e8d53
--- /dev/null
+++ b/tests/e2e/data/documents/harry_potter_part2.txt
@@ -0,0 +1,6 @@
+The Order of the Phoenix, led by Alastor “Mad-Eye” Moody, arrives to take Harry to
+his new home at the Weasleys’ house, the Burrow. Six of Harry’s friends take
+Polyjuice Potion to disguise themselves as Harry and act as decoys, and they all fly
+off in different directions. The Death Eaters, alerted to their departure by Snape,
+attack Harry and his friends. Voldemort chases Harry down, but Harry’s wand fends
+Voldemort off, seemingly without Harry’s help.
diff --git a/tests/e2e/test_entity_resolver_component_e2e.py b/tests/e2e/test_entity_resolver_component_e2e.py
new file mode 100644
index 00000000..49c8af4c
--- /dev/null
+++ b/tests/e2e/test_entity_resolver_component_e2e.py
@@ -0,0 +1,181 @@
+#  Copyright (c) "Neo4j"
+#  Neo4j Sweden AB [https://neo4j.com]
+#  #
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#  #
+#      https://www.apache.org/licenses/LICENSE-2.0
+#  #
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+import neo4j
+import pytest
+from neo4j_graphrag.experimental.components.resolver import (
+    SinglePropertyExactMatchResolver,
+)
+
+
+@pytest.mark.asyncio
+@pytest.mark.usefixtures("setup_neo4j_for_kg_construction")
+async def test_resolver_single_node(driver: neo4j.Driver) -> None:
+    driver.execute_query("MATCH (n) DETACH DELETE n")
+    driver.execute_query(
+        """
+        CREATE (d:Document {id: "0", path: "path"})
+        CREATE (c:Chunk {id: "0:0"})
+        CREATE (c)-[:FROM_DOCUMENT]->(d)
+        CREATE (alice:__Entity__:Person {id: "0:0:1", name: "Alice"})
+        CREATE (alice)-[:FROM_CHUNK]->(c)
+        """
+    )
+    resolver = SinglePropertyExactMatchResolver(driver)
+    res = await resolver.run()
+    # __Entity__ nodes attached to a chunk
+    assert res.number_of_nodes_to_resolve == 1
+    # Alice
+    assert res.number_of_created_nodes == 1
+
+    records, _, _ = driver.execute_query(
+        "MATCH path=(:Person {name: 'Alice'}) RETURN path"
+    )
+    assert len(records) == 1
+    path = records[0].get("path")
+    assert path.start_node.get("name") == "Alice"
+    assert path.start_node.labels == frozenset({"__Entity__", "Person"})
+    assert path.start_node.get("id") == "0:0:1"
+
+
+@pytest.mark.asyncio
+@pytest.mark.usefixtures("setup_neo4j_for_kg_construction")
+async def test_resolver_two_nodes_and_relationships(driver: neo4j.Driver) -> None:
+    driver.execute_query("MATCH (n) DETACH DELETE n")
+    driver.execute_query(
+        """
+        CREATE (d:Document {id: "0", path: "path"})
+        CREATE (c:Chunk {id: "0:0"})
+        CREATE (c)-[:FROM_DOCUMENT]->(d)
+        CREATE (alice1:__Entity__:Person {id: "0:0:1", name: "Alice"})
+        CREATE (alice2:__Entity__:Person {id: "0:0:2", name: "Alice"})
+        CREATE (sweden:__Entity__:Country {id: "0:0:3", name: "Sweden"})
+        CREATE (alice1)-[:LIVES_IN]->(sweden)
+        CREATE (alice1)-[:FROM_CHUNK]->(c)
+        CREATE (alice2)-[:FROM_CHUNK]->(c)
+        CREATE (sweden)-[:FROM_CHUNK]->(c)
+        """
+    )
+    resolver = SinglePropertyExactMatchResolver(driver)
+    res = await resolver.run()
+    # __Entity__ nodes attached to a chunk
+    assert res.number_of_nodes_to_resolve == 3
+    # Alice and Sweden
+    assert res.number_of_created_nodes == 2
+
+    # check the domain graph
+    records, _, _ = driver.execute_query(
+        "MATCH path=(:Person {name: 'Alice'})-[:LIVES_IN]->(:Country {name: 'Sweden'}) RETURN path"
+    )
+    assert len(records) == 1
+    path = records[0].get("path")
+    assert path.start_node.get("name") == "Alice"
+    assert path.start_node.labels == frozenset({"__Entity__", "Person"})
+    assert path.end_node.get("name") == "Sweden"
+    assert path.end_node.labels == frozenset({"__Entity__", "Country"})
+    assert len(path.relationships) == 1
+    assert path.relationships[0].type == "LIVES_IN"
+
+    # check the lexical graph
+    records, _, _ = driver.execute_query(
+        "MATCH path=(:Person {name: 'Alice'})-[:FROM_CHUNK]->(:Chunk) RETURN path"
+    )
+    assert len(records) == 1
+    path = records[0].get("path")
+    assert path.start_node.get("name") == "Alice"
+    assert path.end_node.labels == frozenset({"Chunk"})
+    assert path.end_node.get("id") == "0:0"
+
+
+@pytest.mark.asyncio
+@pytest.mark.usefixtures("setup_neo4j_for_kg_construction")
+async def test_resolver_same_name_different_labels(driver: neo4j.Driver) -> None:
+    driver.execute_query("MATCH (n) DETACH DELETE n")
+    driver.execute_query(
+        """
+        CREATE (d:Document {id: "0", path: "path"})
+        CREATE (c:Chunk {id: "0:0"})
+        CREATE (c)-[:FROM_DOCUMENT]->(d)
+        CREATE (alice1:__Entity__:Person {id: "0:0:1", name: "Alice"})
+        CREATE (alice2:__Entity__:Human {id: "0:0:2", name: "Alice"})
+        CREATE (alice1)-[:FROM_CHUNK]->(c)
+        CREATE (alice2)-[:FROM_CHUNK]->(c)
+        CREATE (sweden)-[:FROM_CHUNK]->(c)
+        """
+    )
+    resolver = SinglePropertyExactMatchResolver(driver)
+    res = await resolver.run()
+    # __Entity__ nodes attached to a chunk
+    assert res.number_of_nodes_to_resolve == 2
+    # Alice Person and Alice Human
+    assert res.number_of_created_nodes == 2
+
+    records, _, _ = driver.execute_query("MATCH (alice {name: 'Alice'}) RETURN alice")
+    assert len(records) == 2
+
+
+@pytest.mark.asyncio
+@pytest.mark.usefixtures("setup_neo4j_for_kg_construction")
+async def test_resolver_custom_property(driver: neo4j.Driver) -> None:
+    driver.execute_query("MATCH (n) DETACH DELETE n")
+    driver.execute_query(
+        """
+        CREATE (d:Document {id: "0", path: "path"})
+        CREATE (c:Chunk {id: "0:0"})
+        CREATE (c)-[:FROM_DOCUMENT]->(d)
+        CREATE (alice:__Entity__:Person {id: "0:0:1", name: "Alice"})
+        CREATE (alicia:__Entity__:Person {id: "0:0:1", name: "Alicia"})
+        CREATE (alice)-[:FROM_CHUNK]->(c)
+        CREATE (alicia)-[:FROM_CHUNK]->(c)
+        """
+    )
+    resolver = SinglePropertyExactMatchResolver(driver, resolve_property="id")
+    res = await resolver.run()
+    # __Entity__ nodes attached to a chunk
+    assert res.number_of_nodes_to_resolve == 2
+    # Alice
+    assert res.number_of_created_nodes == 1
+
+    records, _, _ = driver.execute_query("MATCH (person:Person) RETURN person")
+    assert len(records) == 1
+    assert records[0].get("person").get("name") == "Alice"
+
+
+@pytest.mark.asyncio
+@pytest.mark.usefixtures("setup_neo4j_for_kg_construction")
+async def test_resolver_custom_filter(driver: neo4j.Driver) -> None:
+    driver.execute_query("MATCH (n) DETACH DELETE n")
+    driver.execute_query(
+        """
+        CREATE (d:Document {id: "0", path: "path"})
+        CREATE (c:Chunk {id: "0:0"})
+        CREATE (c)-[:FROM_DOCUMENT]->(d)
+        CREATE (alice1:__Entity__:Person {id: "0:0:1", name: "Alice"})
+        CREATE (alice2:__Entity__:Person {id: "0:0:2", name: "Alice"})
+        CREATE (sweden:__Entity__:Country {id: "0:0:3", name: "Sweden"})
+        CREATE (alice1)-[:LIVES_IN]->(sweden)
+        CREATE (alice1)-[:FROM_CHUNK]->(c)
+        CREATE (alice2)-[:FROM_CHUNK]->(c)
+        CREATE (sweden)-[:FROM_CHUNK]->(c)
+        """
+    )
+    resolver = SinglePropertyExactMatchResolver(
+        driver, filter_query="WHERE not entity:Person"
+    )
+    res = await resolver.run()
+    # __Entity__ nodes attached to a chunk without a Person label
+    # so only Country here
+    assert res.number_of_nodes_to_resolve == 1
+    # Sweden
+    assert res.number_of_created_nodes == 1
diff --git a/tests/e2e/test_kg_builder_pipeline_e2e.py b/tests/e2e/test_kg_builder_pipeline_e2e.py
index 10fa6041..1713098b 100644
--- a/tests/e2e/test_kg_builder_pipeline_e2e.py
+++ b/tests/e2e/test_kg_builder_pipeline_e2e.py
@@ -28,6 +28,9 @@
     OnError,
 )
 from neo4j_graphrag.experimental.components.kg_writer import Neo4jWriter
+from neo4j_graphrag.experimental.components.resolver import (
+    SinglePropertyExactMatchResolver,
+)
 from neo4j_graphrag.experimental.components.schema import (
     SchemaBuilder,
     SchemaEntity,
@@ -63,7 +66,7 @@ def schema_builder() -> SchemaBuilder:
 
 @pytest.fixture
 def text_splitter() -> FixedSizeSplitter:
-    return FixedSizeSplitter(chunk_size=500, chunk_overlap=100)
+    return FixedSizeSplitter(chunk_size=500, chunk_overlap=10)
 
 
 @pytest.fixture
@@ -84,6 +87,11 @@ def kg_writer(driver: neo4j.Driver) -> Neo4jWriter:
     return Neo4jWriter(driver)
 
 
+@pytest.fixture
+def entity_resolver(driver: neo4j.Driver) -> SinglePropertyExactMatchResolver:
+    return SinglePropertyExactMatchResolver(driver)
+
+
 @pytest.fixture
 def kg_builder_pipeline(
     text_splitter: FixedSizeSplitter,
@@ -91,29 +99,16 @@ def kg_builder_pipeline(
     schema_builder: SchemaBuilder,
     entity_relation_extractor: LLMEntityRelationExtractor,
     kg_writer: Neo4jWriter,
+    entity_resolver: SinglePropertyExactMatchResolver,
 ) -> Pipeline:
     pipe = Pipeline()
     # define the components
-    pipe.add_component(
-        text_splitter,
-        "splitter",
-    )
-    pipe.add_component(
-        chunk_embedder,
-        "embedder",
-    )
-    pipe.add_component(
-        schema_builder,
-        "schema",
-    )
-    pipe.add_component(
-        entity_relation_extractor,
-        "extractor",
-    )
-    pipe.add_component(
-        kg_writer,
-        "writer",
-    )
+    pipe.add_component(text_splitter, "splitter")
+    pipe.add_component(chunk_embedder, "embedder")
+    pipe.add_component(schema_builder, "schema")
+    pipe.add_component(entity_relation_extractor, "extractor")
+    pipe.add_component(kg_writer, "writer")
+    pipe.add_component(entity_resolver, "resolver")
     # define the execution order of component
     # and how the output of previous components must be used
     pipe.connect("splitter", "embedder", input_config={"text_chunks": "splitter"})
@@ -125,6 +120,7 @@ def kg_builder_pipeline(
         "writer",
         input_config={"graph": "extractor"},
     )
+    pipe.connect("writer", "resolver", {})
     return pipe
 
 
@@ -135,6 +131,24 @@ def harry_potter_text() -> str:
     return text
 
 
+@pytest.fixture
+def harry_potter_text_part1() -> str:
+    with open(
+        os.path.join(BASE_DIR, "data/documents/harry_potter_part1.txt"), "r"
+    ) as f:
+        text = f.read()
+    return text
+
+
+@pytest.fixture
+def harry_potter_text_part2() -> str:
+    with open(
+        os.path.join(BASE_DIR, "data/documents/harry_potter_part2.txt"), "r"
+    ) as f:
+        text = f.read()
+    return text
+
+
 @pytest.mark.asyncio
 @pytest.mark.usefixtures("setup_neo4j_for_kg_construction")
 async def test_pipeline_builder_happy_path(
@@ -190,7 +204,6 @@ async def test_pipeline_builder_happy_path(
                     }"""
         ),
         LLMResponse(content='{"nodes": [], "relationships": []}'),
-        LLMResponse(content='{"nodes": [], "relationships": []}'),
     ]
 
     # user input:
@@ -252,25 +265,25 @@ async def test_pipeline_builder_happy_path(
     }
     res = await kg_builder_pipeline.run(pipe_inputs)
     # llm must have been called for each chunk
-    assert llm.ainvoke.call_count == 3
+    assert llm.ainvoke.call_count == 2
     # result must be success
     assert isinstance(res, PipelineResult)
     assert res.run_id is not None
-    assert res.result == {"writer": {"status": "SUCCESS"}}
+    assert "resolver" in res.result
     # check component's results
     chunks = await kg_builder_pipeline.store.get_result_for_component(
         res.run_id, "splitter"
     )
-    assert len(chunks["chunks"]) == 3
+    assert len(chunks["chunks"]) == 2
     graph = await kg_builder_pipeline.store.get_result_for_component(
         res.run_id, "extractor"
     )
-    # 3 entities + 3 chunks + 1 document
+    # 3 entities + 2 chunks + 1 document
     nodes = graph["nodes"]
-    assert len(nodes) == 7
+    assert len(nodes) == 6
     label_counts = dict(Counter([n["label"] for n in nodes]))
     assert label_counts == {
-        "Chunk": 3,
+        "Chunk": 2,
         "Document": 1,
         "Person": 2,
         "Organization": 1,
@@ -279,23 +292,23 @@ async def test_pipeline_builder_happy_path(
     # + 3 rels between entities and their chunk
     # + 2 "NEXT_CHUNK" rels
     relationships = graph["relationships"]
-    assert len(relationships) == 10
+    assert len(relationships) == 8
     type_counts = dict(Counter([r["type"] for r in relationships]))
     assert type_counts == {
         "FROM_CHUNK": 3,
-        "FROM_DOCUMENT": 3,
+        "FROM_DOCUMENT": 2,
         "KNOWS": 1,
         "LED_BY": 1,
-        "NEXT_CHUNK": 2,
+        "NEXT_CHUNK": 1,
     }
     # then check content of neo4j db
     created_nodes = driver.execute_query("MATCH (n) RETURN n")
-    assert len(created_nodes.records) == 7
+    assert len(created_nodes.records) == 6
     created_rels = driver.execute_query("MATCH ()-[r]->() RETURN r")
-    assert len(created_rels.records) == 10
+    assert len(created_rels.records) == 8
 
     created_chunks = driver.execute_query("MATCH (n:Chunk) RETURN n").records
-    assert len(created_chunks) == 3
+    assert len(created_chunks) == 2
     for c in created_chunks:
         node = c.get("n")
         assert node.get("embedding") == [1, 2, 3]
@@ -358,7 +371,6 @@ async def test_pipeline_builder_failing_chunk_raise(
                     }"""
         ),
         LLMResponse(content="invalid json"),
-        LLMResponse(content='{"nodes": [], "relationships": []}'),
     ]
 
     # user input:
@@ -437,7 +449,6 @@ async def test_pipeline_builder_failing_chunk_do_not_raise(
                         ]
                     }"""
         ),
-        LLMResponse(content='{"nodes": [], "relationships": []}'),
     ]
 
     # user input:
@@ -458,37 +469,157 @@ async def test_pipeline_builder_failing_chunk_do_not_raise(
     ).component.on_error = OnError.IGNORE  # type: ignore[attr-defined, unused-ignore]
     res = await kg_builder_pipeline.run(pipe_inputs)
     # llm must have been called for each chunk
-    assert llm.ainvoke.call_count == 3
+    assert llm.ainvoke.call_count == 2
     # result must be success
     assert isinstance(res, PipelineResult)
     assert res.run_id is not None
-    assert res.result == {"writer": {"status": "SUCCESS"}}
+    assert res.result == {
+        "resolver": {"number_of_created_nodes": 3, "number_of_nodes_to_resolve": 3}
+    }
     # check component's results
     chunks = await kg_builder_pipeline.store.get_result_for_component(
         res.run_id, "splitter"
     )
-    assert len(chunks["chunks"]) == 3
+    assert len(chunks["chunks"]) == 2
     graph = await kg_builder_pipeline.store.get_result_for_component(
         res.run_id, "extractor"
     )
-    # 3 entities + 3 chunks
+    # 3 entities + 2 chunks
     nodes = graph["nodes"]
-    assert len(nodes) == 6
+    assert len(nodes) == 5
     label_counts = dict(Counter([n["label"] for n in nodes]))
     assert label_counts == {
-        "Chunk": 3,
+        "Chunk": 2,
         "Person": 2,
         "Organization": 1,
     }
     # 2 relationships between entities
     # + 3 rels between entities and their chunk
-    # + 2 "NEXT_CHUNK" rels
+    # + 1 "NEXT_CHUNK" rels
     relationships = graph["relationships"]
-    assert len(relationships) == 7
+    assert len(relationships) == 6
     type_counts = dict(Counter([r["type"] for r in relationships]))
-    assert type_counts == {"FROM_CHUNK": 3, "KNOWS": 1, "LED_BY": 1, "NEXT_CHUNK": 2}
+    assert type_counts == {"FROM_CHUNK": 3, "KNOWS": 1, "LED_BY": 1, "NEXT_CHUNK": 1}
     # then check content of neo4j db
     created_nodes = driver.execute_query("MATCH (n) RETURN n")
-    assert len(created_nodes.records) == 6
+    assert len(created_nodes.records) == 5
     created_rels = driver.execute_query("MATCH ()-[r]->() RETURN r")
-    assert len(created_rels.records) == 7
+    assert len(created_rels.records) == 6
+
+
+@pytest.mark.asyncio
+@pytest.mark.usefixtures("setup_neo4j_for_kg_construction")
+async def test_pipeline_builder_two_documents(
+    harry_potter_text_part1: str,
+    harry_potter_text_part2: str,
+    embedder: MagicMock,
+    llm: MagicMock,
+    driver: neo4j.Driver,
+    kg_builder_pipeline: Pipeline,
+) -> None:
+    """Run same pipeline on two documents. Check entity resolution.
+
+    First document:
+    2 chunks, entities Harry and The Order of the Phoenix, 1 relationship
+    Second document:
+    1 chunk, entities Harry and Alastor Mad-Eye Moody, 1 relationship
+
+    Should create:
+    1 document node
+    3 chunk nodes
+    3 entities (1 Harry + the other two)
+    ==> 7 nodes
+    3 relationships for lexical graph + 3 relationships for the entity graph
+    ==> 6 relationships
+    """
+    driver.execute_query("MATCH (n) DETACH DELETE n")
+    embedder.embed_query.return_value = [1, 2, 3]
+    llm.ainvoke.side_effect = [
+        LLMResponse(
+            content="""{
+                        "nodes": [
+                            {
+                                "id": "0",
+                                "label": "Person",
+                                "properties": {
+                                    "name": "Harry"
+                                }
+                            },
+                            {
+                                "id": "1",
+                                "label": "Organization",
+                                "properties": {
+                                    "name": "The Order of the Phoenix"
+                                }
+                            }
+                        ],
+                        "relationships": [
+                            {
+                                "type": "MEMBER_OF",
+                                "start_node_id": "0",
+                                "end_node_id": "1"
+                            }
+                        ]
+                    }"""
+        ),
+        LLMResponse(
+            content="""{
+                        "nodes": [
+                            {
+                                "id": "10",
+                                "label": "Person",
+                                "properties": {
+                                    "name": "Harry"
+                                }
+                            },
+                            {
+                                "id": "11",
+                                "label": "Person",
+                                "properties": {
+                                    "name": "Alastor Mad-Eye Moody"
+                                }
+                            }
+                        ],
+                        "relationships": [
+                            {
+                                "type": "KNOWS",
+                                "start_node_id": "10",
+                                "end_node_id": "11"
+                            }
+                        ]
+                    }"""
+        ),
+        LLMResponse(content='{"nodes": [], "relationships": []}'),
+    ]
+
+    # user input:
+    # the initial text
+    # and the list of entities and relations we are looking for
+    pipe_inputs_1 = {
+        "splitter": {"text": harry_potter_text_part1},
+        # note: schema not used in this test because
+        # we are mocking the LLM
+        "schema": {
+            "entities": [],
+            "relations": [],
+            "potential_schema": [],
+        },
+    }
+    pipe_inputs_2 = {
+        "splitter": {"text": harry_potter_text_part2},
+        # note: schema not used in this test because
+        # we are mocking the LLM
+        "schema": {
+            "entities": [],
+            "relations": [],
+            "potential_schema": [],
+        },
+    }
+    await kg_builder_pipeline.run(pipe_inputs_1)
+    await kg_builder_pipeline.run(pipe_inputs_2)
+    created_nodes = driver.execute_query("MATCH (n:__Entity__) RETURN n")
+    assert len(created_nodes.records) == 3
+    created_rels = driver.execute_query(
+        "MATCH (:__Entity__)-[r]->(:__Entity__) RETURN r"
+    )
+    assert len(created_rels.records) == 2
diff --git a/tests/e2e/test_kg_writer_component_e2e.py b/tests/e2e/test_kg_writer_component_e2e.py
index c7f495cf..6388dccc 100644
--- a/tests/e2e/test_kg_writer_component_e2e.py
+++ b/tests/e2e/test_kg_writer_component_e2e.py
@@ -28,26 +28,39 @@
 async def test_kg_writer(driver: neo4j.Driver) -> None:
     start_node = Neo4jNode(
         id="1",
-        label="Document",
+        label="MyLabel",
         properties={"chunk": 1},
         embedding_properties={"vectorProperty": [1.0, 2.0, 3.0]},
     )
     end_node = Neo4jNode(
         id="2",
-        label="Document",
-        properties={"chunk": 2},
-        embedding_properties={"vectorProperty": [1.0, 2.0, 3.0]},
+        label="MyLabel",
+        properties={},
+        embedding_properties=None,
     )
     relationship = Neo4jRelationship(
-        start_node_id="1", end_node_id="2", type="NEXT_CHUNK"
+        start_node_id="1", end_node_id="2", type="MY_RELATIONSHIP"
+    )
+    node_with_two_embeddings = Neo4jNode(
+        id="3",
+        label="MyLabel",
+        properties={"chunk": 1},
+        embedding_properties={
+            "vectorProperty": [1.0, 2.0, 3.0],
+            "otherVectorProperty": [10.0, 20.0, 30.0],
+        },
+    )
+    graph = Neo4jGraph(
+        nodes=[start_node, end_node, node_with_two_embeddings],
+        relationships=[relationship],
     )
-    graph = Neo4jGraph(nodes=[start_node, end_node], relationships=[relationship])
 
     neo4j_writer = Neo4jWriter(driver=driver)
-    await neo4j_writer.run(graph=graph)
+    res = await neo4j_writer.run(graph=graph)
+    assert res.status == "SUCCESS"
 
     query = """
-    MATCH (a:Document {id: '1'})-[r:NEXT_CHUNK]->(b:Document {id: '2'})
+    MATCH (a:MyLabel {id: '1'})-[r:MY_RELATIONSHIP]->(b:MyLabel {id: '2'})
     RETURN a, r, b
     """
     record = driver.execute_query(query).records[0]
@@ -56,11 +69,10 @@ async def test_kg_writer(driver: neo4j.Driver) -> None:
     node_a = record["a"]
     assert start_node.label in list(node_a.labels)
     assert start_node.id == str(node_a.get("id"))
-    if start_node.properties:
-        for key, val in start_node.properties.items():
-            assert key in node_a.keys()
-            assert val == node_a.get(key)
-    if start_node.embedding_properties:
+    for key, val in start_node.properties.items():
+        assert key in node_a.keys()
+        assert val == node_a.get(key)
+    if start_node.embedding_properties:  # for mypy
         for key, val in start_node.embedding_properties.items():
             assert key in node_a.keys()
             assert node_a.get(key) == [1.0, 2.0, 3.0]
@@ -68,17 +80,23 @@ async def test_kg_writer(driver: neo4j.Driver) -> None:
     node_b = record["b"]
     assert end_node.label in list(node_b.labels)
     assert end_node.id == str(node_b.get("id"))
-    if end_node.properties:
-        for key, val in end_node.properties.items():
-            assert key in node_b.keys()
-            assert val == node_b.get(key)
-    if end_node.embedding_properties:
-        for key, val in end_node.embedding_properties.items():
-            assert key in node_b.keys()
-            assert node_b.get(key) == [1.0, 2.0, 3.0]
+    for key, val in end_node.properties.items():
+        assert key in node_b.keys()
+        assert val == node_b.get(key)
 
     rel = record["r"]
     assert rel.type == relationship.type
-    assert relationship.start_node_id and relationship.end_node_id in [
-        str(node.get("id")) for node in rel.nodes
-    ]
+    assert relationship.start_node_id == rel.start_node.get("id")
+    assert relationship.end_node_id == rel.end_node.get("id")
+
+    query = """
+    MATCH (c:MyLabel {id: '3'})
+    RETURN c
+    """
+    records = driver.execute_query(query).records
+    assert len(records) == 1
+    node_c = records[0]["c"]
+    if node_with_two_embeddings.embedding_properties:  # for mypy
+        for key, val in node_with_two_embeddings.embedding_properties.items():
+            assert key in node_c.keys()
+            assert val == node_c.get(key)
diff --git a/tests/unit/experimental/components/test_kg_writer.py b/tests/unit/experimental/components/test_kg_writer.py
index ee1f49ff..94271d5a 100644
--- a/tests/unit/experimental/components/test_kg_writer.py
+++ b/tests/unit/experimental/components/test_kg_writer.py
@@ -18,7 +18,7 @@
 from unittest.mock import MagicMock, Mock
 
 import pytest
-from neo4j_graphrag.experimental.components.kg_writer import Neo4jWriter
+from neo4j_graphrag.experimental.components.kg_writer import Neo4jWriter, batched
 from neo4j_graphrag.experimental.components.types import (
     Neo4jGraph,
     Neo4jNode,
@@ -27,17 +27,41 @@
 from neo4j_graphrag.neo4j_queries import UPSERT_NODE_QUERY, UPSERT_RELATIONSHIP_QUERY
 
 
+def test_batched() -> None:
+    assert list(batched([1, 2, 3, 4], batch_size=2)) == [
+        [1, 2],
+        [3, 4],
+    ]
+    assert list(batched([1, 2, 3], batch_size=2)) == [
+        [1, 2],
+        [3],
+    ]
+    assert list(batched([1, 2, 3], batch_size=4)) == [
+        [1, 2, 3],
+    ]
+
+
 @mock.patch(
     "neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter._db_setup",
     return_value=None,
 )
-def test_upsert_node(driver: MagicMock) -> None:
+def test_upsert_nodes(driver: MagicMock) -> None:
     neo4j_writer = Neo4jWriter(driver=driver)
     node = Neo4jNode(id="1", label="Label", properties={"key": "value"})
-    neo4j_writer._upsert_node(node=node)
+    neo4j_writer._upsert_nodes(nodes=[node])
     driver.execute_query.assert_called_once_with(
-        UPSERT_NODE_QUERY.format(label="Label"),
-        parameters_={"id": "1", "properties": {"key": "value"}, "embeddings": None},
+        UPSERT_NODE_QUERY,
+        parameters_={
+            "rows": [
+                {
+                    "label": "Label",
+                    "labels": ["Label", "__Entity__"],
+                    "id": "1",
+                    "properties": {"key": "value"},
+                    "embedding_properties": None,
+                }
+            ]
+        },
     )
 
 
@@ -45,7 +69,7 @@ def test_upsert_node(driver: MagicMock) -> None:
     "neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter._db_setup",
     return_value=None,
 )
-def test_upsert_node_with_embedding(
+def test_upsert_nodes_with_embedding(
     driver: MagicMock,
 ) -> None:
     neo4j_writer = Neo4jWriter(driver=driver)
@@ -56,13 +80,19 @@ def test_upsert_node_with_embedding(
         embedding_properties={"embeddingProp": [1.0, 2.0, 3.0]},
     )
     driver.execute_query.return_value.records = [{"elementId(n)": 1}]
-    neo4j_writer._upsert_node(node=node)
+    neo4j_writer._upsert_nodes(nodes=[node])
     driver.execute_query.assert_any_call(
-        UPSERT_NODE_QUERY.format(label="Label"),
+        UPSERT_NODE_QUERY,
         parameters_={
-            "id": "1",
-            "properties": {"key": "value"},
-            "embeddings": {"embeddingProp": [1.0, 2.0, 3.0]},
+            "rows": [
+                {
+                    "label": "Label",
+                    "labels": ["Label", "__Entity__"],
+                    "id": "1",
+                    "properties": {"key": "value"},
+                    "embedding_properties": {"embeddingProp": [1.0, 2.0, 3.0]},
+                }
+            ]
         },
     )
 
@@ -79,18 +109,20 @@ def test_upsert_relationship(driver: MagicMock) -> None:
         type="RELATIONSHIP",
         properties={"key": "value"},
     )
-    neo4j_writer._upsert_relationship(rel=rel)
+    neo4j_writer._upsert_relationships(rels=[rel])
     parameters = {
-        "start_node_id": "1",
-        "end_node_id": "2",
-        "properties": {"key": "value"},
-        "embeddings": None,
+        "rows": [
+            {
+                "type": "RELATIONSHIP",
+                "start_node_id": "1",
+                "end_node_id": "2",
+                "properties": {"key": "value"},
+                "embedding_properties": None,
+            }
+        ]
     }
     driver.execute_query.assert_called_once_with(
-        UPSERT_RELATIONSHIP_QUERY.format(
-            type="RELATIONSHIP",
-            properties="{key: $key}",
-        ),
+        UPSERT_RELATIONSHIP_QUERY,
         parameters_=parameters,
     )
 
@@ -109,18 +141,20 @@ def test_upsert_relationship_with_embedding(_: Mock, driver: MagicMock) -> None:
         embedding_properties={"embeddingProp": [1.0, 2.0, 3.0]},
     )
     driver.execute_query.return_value.records = [{"elementId(r)": "rel_elem_id"}]
-    neo4j_writer._upsert_relationship(rel=rel)
+    neo4j_writer._upsert_relationships(rels=[rel])
     parameters = {
-        "start_node_id": "1",
-        "end_node_id": "2",
-        "properties": {"key": "value"},
-        "embeddings": {"embeddingProp": [1.0, 2.0, 3.0]},
+        "rows": [
+            {
+                "type": "RELATIONSHIP",
+                "start_node_id": "1",
+                "end_node_id": "2",
+                "properties": {"key": "value"},
+                "embedding_properties": {"embeddingProp": [1.0, 2.0, 3.0]},
+            }
+        ]
     }
     driver.execute_query.assert_any_call(
-        UPSERT_RELATIONSHIP_QUERY.format(
-            type="RELATIONSHIP",
-            properties="{key: $key}",
-        ),
+        UPSERT_RELATIONSHIP_QUERY,
         parameters_=parameters,
     )
 
@@ -137,17 +171,32 @@ async def test_run(_: Mock, driver: MagicMock) -> None:
     graph = Neo4jGraph(nodes=[node], relationships=[rel])
     await neo4j_writer.run(graph=graph)
     driver.execute_query.assert_any_call(
-        UPSERT_NODE_QUERY.format(label="Label"),
-        parameters_={"id": "1", "properties": {}, "embeddings": None},
+        UPSERT_NODE_QUERY,
+        parameters_={
+            "rows": [
+                {
+                    "label": "Label",
+                    "labels": ["Label", "__Entity__"],
+                    "id": "1",
+                    "properties": {},
+                    "embedding_properties": None,
+                }
+            ]
+        },
     )
     parameters_ = {
-        "start_node_id": "1",
-        "end_node_id": "2",
-        "properties": {},
-        "embeddings": None,
+        "rows": [
+            {
+                "type": "RELATIONSHIP",
+                "start_node_id": "1",
+                "end_node_id": "2",
+                "properties": {},
+                "embedding_properties": None,
+            }
+        ]
     }
     driver.execute_query.assert_any_call(
-        UPSERT_RELATIONSHIP_QUERY.format(type="RELATIONSHIP", properties="{}"),
+        UPSERT_RELATIONSHIP_QUERY,
         parameters_=parameters_,
     )
 
@@ -164,16 +213,31 @@ async def test_run_async_driver(_: Mock, async_driver: MagicMock) -> None:
     graph = Neo4jGraph(nodes=[node], relationships=[rel])
     await neo4j_writer.run(graph=graph)
     async_driver.execute_query.assert_any_call(
-        UPSERT_NODE_QUERY.format(label="Label"),
-        parameters_={"id": "1", "properties": {}, "embeddings": None},
+        UPSERT_NODE_QUERY,
+        parameters_={
+            "rows": [
+                {
+                    "label": "Label",
+                    "labels": ["Label", "__Entity__"],
+                    "id": "1",
+                    "properties": {},
+                    "embedding_properties": None,
+                }
+            ]
+        },
     )
     parameters_ = {
-        "start_node_id": "1",
-        "end_node_id": "2",
-        "properties": {},
-        "embeddings": None,
+        "rows": [
+            {
+                "type": "RELATIONSHIP",
+                "start_node_id": "1",
+                "end_node_id": "2",
+                "properties": {},
+                "embedding_properties": None,
+            }
+        ]
     }
     async_driver.execute_query.assert_any_call(
-        UPSERT_RELATIONSHIP_QUERY.format(type="RELATIONSHIP", properties="{}"),
+        UPSERT_RELATIONSHIP_QUERY,
         parameters_=parameters_,
     )
diff --git a/tests/unit/experimental/components/test_resolver.py b/tests/unit/experimental/components/test_resolver.py
new file mode 100644
index 00000000..558b23f5
--- /dev/null
+++ b/tests/unit/experimental/components/test_resolver.py
@@ -0,0 +1,59 @@
+#  Copyright (c) "Neo4j"
+#  Neo4j Sweden AB [https://neo4j.com]
+#  #
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#  #
+#      https://www.apache.org/licenses/LICENSE-2.0
+#  #
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+from unittest.mock import MagicMock, call
+
+import neo4j
+import pytest
+from neo4j_graphrag.experimental.components.resolver import (
+    SinglePropertyExactMatchResolver,
+)
+from neo4j_graphrag.experimental.components.types import ResolutionStats
+
+
+@pytest.mark.asyncio
+async def test_simple_resolver(driver: MagicMock) -> None:
+    driver.execute_query.side_effect = [
+        ([neo4j.Record({"c": 2})], None, None),
+        ([neo4j.Record({"c": 1})], None, None),
+    ]
+    resolver = SinglePropertyExactMatchResolver(driver=driver)
+    res = await resolver.run()
+    assert isinstance(res, ResolutionStats)
+    assert res.number_of_nodes_to_resolve == 2
+    assert res.number_of_created_nodes == 1
+    assert driver.execute_query.call_count == 2
+    driver.execute_query.assert_has_calls(
+        [call("MATCH (entity:__Entity__)  RETURN count(entity) as c", database_=None)]
+    )
+
+
+@pytest.mark.asyncio
+async def test_simple_resolver_custom_filter(driver: MagicMock) -> None:
+    driver.execute_query.side_effect = [
+        ([neo4j.Record({"c": 2})], None, None),
+        ([neo4j.Record({"c": 1})], None, None),
+    ]
+    resolver = SinglePropertyExactMatchResolver(
+        driver=driver, filter_query="WHERE not entity:Resolved"
+    )
+    await resolver.run()
+    driver.execute_query.assert_has_calls(
+        [
+            call(
+                "MATCH (entity:__Entity__) WHERE not entity:Resolved RETURN count(entity) as c",
+                database_=None,
+            )
+        ]
+    )
diff --git a/tests/unit/experimental/pipeline/test_kg_builder.py b/tests/unit/experimental/pipeline/test_kg_builder.py
new file mode 100644
index 00000000..f509be6b
--- /dev/null
+++ b/tests/unit/experimental/pipeline/test_kg_builder.py
@@ -0,0 +1,256 @@
+#  Copyright (c) "Neo4j"
+#  Neo4j Sweden AB [https://neo4j.com]
+#  #
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#  #
+#      https://www.apache.org/licenses/LICENSE-2.0
+#  #
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+from unittest.mock import MagicMock, patch
+
+import neo4j
+import pytest
+from neo4j_graphrag.experimental.components.entity_relation_extractor import OnError
+from neo4j_graphrag.experimental.components.schema import SchemaEntity, SchemaRelation
+from neo4j_graphrag.experimental.pipeline.exceptions import PipelineDefinitionError
+from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline
+from neo4j_graphrag.experimental.pipeline.pipeline import PipelineResult
+from neo4j_graphrag.llm.base import LLMInterface
+
+
+@pytest.mark.asyncio
+async def test_knowledge_graph_builder_init_with_text() -> None:
+    llm = MagicMock(spec=LLMInterface)
+    driver = MagicMock(spec=neo4j.Driver)
+
+    kg_builder = SimpleKGPipeline(
+        llm=llm,
+        driver=driver,
+        from_pdf=False,
+    )
+
+    assert kg_builder.llm == llm
+    assert kg_builder.driver == driver
+    assert kg_builder.from_pdf is False
+    assert kg_builder.entities == []
+    assert kg_builder.relations == []
+    assert kg_builder.potential_schema == []
+    assert "pdf_loader" not in kg_builder.pipeline
+
+    text_input = "May thy knife chip and shatter."
+
+    with patch.object(
+        kg_builder.pipeline,
+        "run",
+        return_value=PipelineResult(run_id="test_run", result=None),
+    ) as mock_run:
+        await kg_builder.run_async(text=text_input)
+        mock_run.assert_called_once()
+        pipe_inputs = mock_run.call_args[0][0]
+        assert pipe_inputs["splitter"]["text"] == text_input
+
+
+@pytest.mark.asyncio
+async def test_knowledge_graph_builder_init_with_file_path() -> None:
+    llm = MagicMock(spec=LLMInterface)
+    driver = MagicMock(spec=neo4j.Driver)
+
+    kg_builder = SimpleKGPipeline(
+        llm=llm,
+        driver=driver,
+        from_pdf=True,
+    )
+
+    assert kg_builder.llm == llm
+    assert kg_builder.driver == driver
+    assert kg_builder.from_pdf is True
+    assert kg_builder.entities == []
+    assert kg_builder.relations == []
+    assert kg_builder.potential_schema == []
+    assert "pdf_loader" in kg_builder.pipeline
+
+    file_path = "path/to/test.pdf"
+
+    with patch.object(
+        kg_builder.pipeline,
+        "run",
+        return_value=PipelineResult(run_id="test_run", result=None),
+    ) as mock_run:
+        await kg_builder.run_async(file_path=file_path)
+        mock_run.assert_called_once()
+        pipe_inputs = mock_run.call_args[0][0]
+        assert pipe_inputs["pdf_loader"]["filepath"] == file_path
+
+
+@pytest.mark.asyncio
+async def test_knowledge_graph_builder_run_with_both_inputs() -> None:
+    llm = MagicMock(spec=LLMInterface)
+    driver = MagicMock(spec=neo4j.Driver)
+
+    kg_builder = SimpleKGPipeline(
+        llm=llm,
+        driver=driver,
+        from_pdf=True,
+    )
+
+    text_input = "May thy knife chip and shatter."
+    file_path = "path/to/test.pdf"
+
+    with pytest.raises(PipelineDefinitionError) as exc_info:
+        await kg_builder.run_async(file_path=file_path, text=text_input)
+
+    assert "Expected 'file_path' argument when 'from_pdf' is True." in str(
+        exc_info.value
+    ) or "Expected 'text' argument when 'from_pdf' is False." in str(exc_info.value)
+
+
+@pytest.mark.asyncio
+async def test_knowledge_graph_builder_run_with_no_inputs() -> None:
+    llm = MagicMock(spec=LLMInterface)
+    driver = MagicMock(spec=neo4j.Driver)
+
+    kg_builder = SimpleKGPipeline(
+        llm=llm,
+        driver=driver,
+        from_pdf=True,  # or False
+    )
+
+    with pytest.raises(PipelineDefinitionError) as exc_info:
+        await kg_builder.run_async()
+
+    assert "Expected 'file_path' argument when 'from_pdf' is True." in str(
+        exc_info.value
+    ) or "Expected 'text' argument when 'from_pdf' is False." in str(exc_info.value)
+
+
+@pytest.mark.asyncio
+async def test_knowledge_graph_builder_document_info_with_file() -> None:
+    llm = MagicMock(spec=LLMInterface)
+    driver = MagicMock(spec=neo4j.Driver)
+
+    kg_builder = SimpleKGPipeline(
+        llm=llm,
+        driver=driver,
+        from_pdf=True,
+    )
+
+    file_path = "path/to/test.pdf"
+
+    with patch.object(
+        kg_builder.pipeline,
+        "run",
+        return_value=PipelineResult(run_id="test_run", result=None),
+    ) as mock_run:
+        await kg_builder.run_async(file_path=file_path)
+
+        pipe_inputs = mock_run.call_args[0][0]
+        assert "pdf_loader" in pipe_inputs
+        assert pipe_inputs["pdf_loader"] == {"filepath": file_path}
+        assert "extractor" not in pipe_inputs
+
+
+@pytest.mark.asyncio
+async def test_knowledge_graph_builder_document_info_with_text() -> None:
+    llm = MagicMock(spec=LLMInterface)
+    driver = MagicMock(spec=neo4j.Driver)
+
+    kg_builder = SimpleKGPipeline(
+        llm=llm,
+        driver=driver,
+        from_pdf=False,
+    )
+
+    text_input = "May thy knife chip and shatter."
+
+    with patch.object(
+        kg_builder.pipeline,
+        "run",
+        return_value=PipelineResult(run_id="test_run", result=None),
+    ) as mock_run:
+        await kg_builder.run_async(text=text_input)
+
+        pipe_inputs = mock_run.call_args[0][0]
+        assert "splitter" in pipe_inputs
+        assert pipe_inputs["splitter"] == {"text": text_input}
+
+
+@pytest.mark.asyncio
+async def test_knowledge_graph_builder_with_entities_and_file() -> None:
+    llm = MagicMock(spec=LLMInterface)
+    driver = MagicMock(spec=neo4j.Driver)
+
+    entities = ["Document", "Section"]
+    relations = ["CONTAINS"]
+    potential_schema = [("Document", "CONTAINS", "Section")]
+
+    kg_builder = SimpleKGPipeline(
+        llm=llm,
+        driver=driver,
+        entities=entities,
+        relations=relations,
+        potential_schema=potential_schema,
+        from_pdf=True,
+    )
+
+    internal_entities = [SchemaEntity(label=label) for label in entities]
+    internal_relations = [SchemaRelation(label=label) for label in relations]
+    assert kg_builder.entities == internal_entities
+    assert kg_builder.relations == internal_relations
+    assert kg_builder.potential_schema == potential_schema
+
+    file_path = "path/to/test.pdf"
+
+    with patch.object(
+        kg_builder.pipeline,
+        "run",
+        return_value=PipelineResult(run_id="test_run", result=None),
+    ) as mock_run:
+        await kg_builder.run_async(file_path=file_path)
+        pipe_inputs = mock_run.call_args[0][0]
+        assert pipe_inputs["schema"]["entities"] == internal_entities
+        assert pipe_inputs["schema"]["relations"] == internal_relations
+        assert pipe_inputs["schema"]["potential_schema"] == potential_schema
+
+
+def test_simple_kg_pipeline_on_error_conversion() -> None:
+    llm = MagicMock(spec=LLMInterface)
+    driver = MagicMock(spec=neo4j.Driver)
+
+    kg_builder = SimpleKGPipeline(
+        llm=llm,
+        driver=driver,
+        on_error="RAISE",
+    )
+
+    assert kg_builder.on_error == OnError.RAISE
+
+
+def test_simple_kg_pipeline_on_error_invalid_value() -> None:
+    llm = MagicMock(spec=LLMInterface)
+    driver = MagicMock(spec=neo4j.Driver)
+
+    with pytest.raises(PipelineDefinitionError) as exc_info:
+        SimpleKGPipeline(
+            llm=llm,
+            driver=driver,
+            on_error="IGNORE",
+        )
+
+    assert "Expected 'RAISE' or 'CONTINUE'" in str(exc_info.value)
+
+
+def test_simple_kg_pipeline_no_entity_resolution() -> None:
+    llm = MagicMock(spec=LLMInterface)
+    driver = MagicMock(spec=neo4j.Driver)
+
+    kg_builder = SimpleKGPipeline(
+        llm=llm, driver=driver, on_error="CONTINUE", perform_entity_resolution=False
+    )
+
+    assert "resolver" not in kg_builder.pipeline