> ## Documentation Index > Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt > Use this file to discover all available pages before exploring further. # Review Conflicting Facts > Use improve(review_conflicts=True) to find facts that disagree across dated sources and inspect the conflicts Cognee stores A minimal guide to reviewing conflicting facts with `improve()`. Use it when two sources in the same dataset assert different values for one attribute — here, two documents naming different CEOs for the same company — and you want Cognee to record the disagreement, ordered by each source's date, instead of silently keeping both. ## Before You Start * Complete [Quickstart](/getting-started/quickstart) to understand basic operations * Ensure you have [LLM Providers](/setup-configuration/llm-providers) configured — the review stage is skipped when no LLM is available * Read [Improve](/python-api/improve) for how `improve()` runs its stages, including `review_conflicts` * Keep [`EDGE_EVIDENCE_ENABLED`](/setup-configuration/overview#edge-evidence) at its default `true` — the review stage is skipped when edge evidence is off ## Code in Action ```python theme={null} import asyncio from uuid import uuid4 import cognee from cognee.context_global_variables import set_database_global_context_variables from cognee.infrastructure.databases.graph import get_graph_engine from cognee.modules.data.methods import get_authorized_existing_datasets from cognee.modules.engine.operations.setup import setup from cognee.modules.graph.utils.fact_conflicts import read_conflict_marks from cognee.modules.improve.config import get_improve_config from cognee.modules.users.methods import get_default_user from cognee.tasks.ingestion.data_item import DataItem async def main(): await setup() dataset_name = f"review_conflicts_{uuid4().hex[:8]}" user = await get_default_user() date_key = get_improve_config().effective_date_key for text, date, label in [ ("Acme is led by CEO Alice.", "2020-05-01", "2020 directory"), ("Acme is led by CEO Bob.", "2026-06-01", "2026 board notice"), ]: await cognee.remember( DataItem(text, label=label, external_metadata={date_key: date}, literal_text=True), dataset_name=dataset_name, user=user, self_improvement=False, custom_prompt="Extract the company Acme and the named person. Connect Acme to its CEO with has_ceo.", ) result = await cognee.improve(dataset=dataset_name, user=user, review_conflicts=True) print(dataset_name, result.stage("review_conflicts")) dataset = ( await get_authorized_existing_datasets( user=user, datasets=[dataset_name], permission_type="read" ) )[0] async with set_database_global_context_variables(dataset.id, dataset.owner_id): graph = await get_graph_engine() nodes, edges = await graph.get_graph_data() conflict_ids = set() for node_id, properties in nodes: if properties.get("type") == "FactConflict" and str( properties.get("dataset_id") ) == str(dataset.id): conflict_ids.add(str(node_id)) print("Conflict:", properties) for source, target, relationship, properties in edges: if str(source) in conflict_ids: print("Link:", relationship, target, properties) marks = [ mark for mark in read_conflict_marks(properties) if mark.get("conflict_id") in conflict_ids ] if marks: print("Fact:", properties.get("edge_text"), marks) repeat = await cognee.improve(dataset=dataset_name, user=user, review_conflicts=True) print("Unchanged repeat:", repeat.stage("review_conflicts")) if __name__ == "__main__": asyncio.run(main()) ``` The dataset is kept after the run so you can inspect it, and every run uses a fresh dataset name. ## What Just Happened ### Step 1: Remember Dated Facts ```python theme={null} date_key = get_improve_config().effective_date_key for text, date, label in [ ("Acme is led by CEO Alice.", "2020-05-01", "2020 directory"), ("Acme is led by CEO Bob.", "2026-06-01", "2026 board notice"), ]: await cognee.remember( DataItem(text, label=label, external_metadata={date_key: date}, literal_text=True), dataset_name=dataset_name, user=user, self_improvement=False, custom_prompt="Extract the company Acme and the named person. Connect Acme to its CEO with has_ceo.", ) ``` Each source is wrapped in a `DataItem` whose `external_metadata` carries the date the source's claims hold as of, under the key the review reads (`effective_date` by default, configurable with `IMPROVE_EFFECTIVE_DATE_KEY`). `self_improvement=False` keeps `remember()` from improving on its own, so the review runs only when the script asks for it. ### Step 2: Run the Conflict Review ```python theme={null} result = await cognee.improve(dataset=dataset_name, user=user, review_conflicts=True) print(dataset_name, result.stage("review_conflicts")) ``` `review_conflicts=True` opts in to the `review_conflicts` stage of `improve()`, which is off by default (`IMPROVE_REVIEW_CONFLICTS=true` enables it too). An LLM reviews the dataset's source facts and their dates, stores each disagreement it accepts as a `FactConflict` node, and updates the affected entity descriptions. `result.stage("review_conflicts")` returns that stage's result, including its status. ### Step 3: Inspect the Stored Conflicts ```python theme={null} for node_id, properties in nodes: if properties.get("type") == "FactConflict" and str( properties.get("dataset_id") ) == str(dataset.id): conflict_ids.add(str(node_id)) print("Conflict:", properties) for source, target, relationship, properties in edges: if str(source) in conflict_ids: print("Link:", relationship, target, properties) marks = [ mark for mark in read_conflict_marks(properties) if mark.get("conflict_id") in conflict_ids ] if marks: print("Fact:", properties.get("edge_text"), marks) ``` Each `FactConflict` node describes one attribute of one entity with competing values, and its outgoing edges link it to the entity it is about, the competing values, and the source chunks it cites. The facts themselves carry conflict marks — `read_conflict_marks()` reads them on any graph backend — that record each fact's status within the conflict: `current`, `superseded`, or `conflicting`. ### Step 4: Repeat on Unchanged Data ```python theme={null} repeat = await cognee.improve(dataset=dataset_name, user=user, review_conflicts=True) print("Unchanged repeat:", repeat.stage("review_conflicts")) ``` The review keeps a watermark per dataset and re-reviews only entities whose facts changed since the last run. With no new facts and no unfinished conflicts to repair, the second run reports the stage as `already_completed` instead of calling the LLM again. How `improve()` runs its stages and what each one does. The other time axis: when a fact happened, not when its source dated it. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.