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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Do not let a Graph RAG system silently choose between conflicting facts or treat old source material as current. Keep each assertion tied to its source passage, scope, and time; classify disagreements; apply a documented resolution policy; and show unresolved conflicts with their evidence. When a source changes, refresh the graph material derived from it while retaining older versions if users need historical answers.
Why Graph RAG needs an explicit conflict policy
A knowledge graph organizes entities, relationships, and other extracted information. It does not, by itself, prove which of two incompatible statements is true. Graph RAG can help find relevant evidence across a corpus, but the retrieved evidence still has to be interpreted in context.
This matters because graph construction can compress source detail. In Microsoft’s documented GraphRAG pipeline, entity and relationship descriptions are summarized; claim extraction is a separate, optional feature and is off by default. Microsoft also notes that claim extraction generally requires prompt tuning. A concise entity summary may therefore omit a qualification or disagreement that is present in the underlying documents.
Microsoft’s GraphRAG Responsible AI Transparency guidance says that human analysis is important for reliable insights and that provenance should be traced to verify generated inferences. Treat the graph as an evidence and retrieval structure—not as an automatic arbiter of truth.
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How to represent competing claims
Keep assertions separate from summaries
Store source-grounded assertions as distinct claims rather than overwriting one value with a merged entity description. A useful implementation can record each claim’s text, subject, predicate, object or value, source pointer, and extraction metadata. These are design choices, not a prescribed GraphRAG schema. The key principle is that incompatible assertions remain independently inspectable.
Microsoft’s GraphRAG dataflow describes claims as positive factual statements with an evaluated status and time bounds. Even if your implementation does not use Microsoft’s optional claim-extraction feature, a separate claim layer helps preserve distinctions that a summary could flatten.
Preserve the evidence chain
For every claim, retain a stable source identifier and a pointer to the supporting document passage or text unit. Keep the original document accessible, not just an extracted sentence or generated summary. Record publication or effective dates, source version, and retrieval date when available. Microsoft’s dataflow links documents to text units, and its responsible-use guidance recommends checking provenance when verifying inferences.
This lets a reviewer tell whether the graph assertion accurately reflects its cited passage, whether the passage is authoritative for the question, and whether a later document changed the answer.
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Record time and scope
For facts that change, record the period during which each claim applies when that can be established. Also preserve scope that may explain apparent disagreement: for example, jurisdiction, organization, product version, population, or definition. A statement about one product release or region may not contradict a statement about another.
If users need historical answers, distinguish when a fact applied from when the system learned or recorded it. That bitemporal model—valid time versus recorded time—is a useful design recommendation, not a documented default in Microsoft GraphRAG. Microsoft’s dataflow does describe claim status and time bounds, while the T-GRAG research proposal explores timestamped evolving graphs and temporal query decomposition.
How to investigate a conflict
Classify the disagreement before deciding what to answer. The following practical categories reflect common problem dimensions; they are not presented as the exact category names in Google Research’s 2025 conflict study.
- Temporal: the statements apply at different dates or product versions.
- Scope-related: they refer to different regions, populations, organizations, or definitions.
- Source contradiction: both sources address the same scope and period but make incompatible claims.
- Extraction or interpretation error: the graph assertion overstates or misreads the passage attached to it.
- Coverage gap: the available evidence does not establish which claim applies, or whether either claim is complete.
Google Research’s 2025 work, “(D)RAGged Into a Conflict,” examines conflict types and reports that providing a model with a conflict category improved response quality and appropriateness in its experiments. The authors also report substantial room for improvement. The practical implication is to make the kind of disagreement visible to the answering system rather than treating every mismatch as the same problem.
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Check the source passage, not only the graph record
- Retrieve each claim’s supporting passage. Confirm that the cited text actually says what the graph assertion says.
- Compare dates and scope. Look for different effective periods, versions, jurisdictions, populations, or definitions.
- Check source status. Determine whether a document is a draft, final policy, replacement, or historical record, where that information is available.
- Apply your documented source policy. Weigh direct evidence and the source’s authority for that domain, plus scope, effective date, and any explicit supersession.
- Keep the conflict unresolved when evidence does not decide it. Do not silently discard one claim merely because another is newer or easier to retrieve.
There is no universal source hierarchy established by the reviewed GraphRAG guidance. Recency is a signal, not a truth score: a new draft or proposal does not necessarily supersede an older, active policy. If two authoritative sources remain incompatible, retain both and explain the disagreement.
How to keep changed sources from going stale
A source update can affect more than one graph record. Microsoft’s documented dataflow runs from source documents through text units, graph extraction, claims, communities, reports, and embeddings. That dependency chain is a useful basis for update handling, though Microsoft does not prescribe one universal invalidation policy.
- Version the changed source. Preserve the prior version and mark its status—for example, historical or superseded—rather than erasing the evidence needed for an earlier “as of” question.
- Identify dependent material. Find claims, entity or relationship descriptions, summaries, reports, and retrieval artifacts that depend on the changed text.
- Reprocess changed text. Extract or update claims from the revised passage and preserve the provenance of the new assertions.
- Refresh affected summaries and retrieval artifacts. Regenerate material that depends on changed facts so an old summary does not continue to present them as current.
- Test current and historical questions. Confirm that current queries use the new version and that historical queries can still retrieve the earlier evidence when appropriate.
For a minor edit, rebuilding every index may be unnecessary if the implementation can reliably identify dependencies. For a broad change or uncertain dependency tracking, a fuller refresh may be safer. The right choice depends on the system’s indexing design; the important requirement is not to leave downstream material silently inconsistent with its source.
How to answer when evidence disagrees
When the evidence and source policy support one claim, answer with that claim and include the relevant date and scope. When the disagreement remains unresolved, describe the competing statements, identify their sources and dates, and say what remains uncertain. If the evidence does not support a confident answer, qualify it or abstain rather than quietly selecting a winner.
Keep citations close to the claims they support and make them traceable to source passages. Do not cite only a generated community summary when the underlying passage is available. This gives readers a way to inspect the evidence and helps reviewers find errors in extraction or interpretation.
How to evaluate conflict handling
Test the system on contradictions and source updates drawn from the corpus it is meant to serve. Evaluate the evidence path as well as the final answer; ordinary answer accuracy alone can miss a system that retrieved conflicting sources but hid one of them.
- Conflict retrieval: Does the system find both relevant claims when documents disagree?
- Provenance: Can reviewers trace each returned claim to its source passage?
- Temporal behavior: Does an “as of” question retrieve the claim valid at the requested time?
- Supersession: After a source changes, does the system stop presenting superseded material as current while retaining it for historical questions?
- Calibrated answers: Does it explain unresolved disagreements or abstain when the evidence cannot decide them?
- Claim-level grounding: Are the statements in the answer supported by the returned source material?
Microsoft describes evaluating GraphRAG through manual inspection and gold answers, answer coverage and human inspection of returned context, and claim coverage with source inspection for hallucinations. Google Research’s 2025 work argues for evaluating conflict management alongside factual correctness. Neither establishes a universal pass threshold for a particular application, so set thresholds against your own risk and corpus.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What GraphRAG variants do—and do not—guarantee
| Approach | Documented distinction | Implication for conflicts |
|---|---|---|
| Microsoft standard GraphRAG | Uses an LLM for entity and relationship extraction and summarization; optional claim extraction is available and off by default. | Can preserve claims if configured, but extraction and summarization do not themselves settle contradictions. |
| Microsoft FastGraphRAG | Uses NLP and co-occurrence extraction for some tasks instead of LLM reasoning; it does not use claim extraction. | It is a different indexing choice, not a guarantee of better conflict resolution. |
| T-GRAG | A research proposal focused on timestamped evolving graphs and temporal query decomposition. | It addresses temporal ambiguity as a research direction; it should not be treated as an equivalent, production-validated product. |
Microsoft’s documentation also cautions that effective indexing depends on well-constructed prompts and that domain-specific concepts may need tailored prompts. Its responsible-use guidance describes GraphRAG as most effective with natural-language text focused on an overall topic and rich in identifiable entities.
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Project status and practical limits
Microsoft’s GraphRAG repository describes the project as largely in maintenance mode, warns that indexing may be expensive, and says the code is a demonstration rather than an officially supported Microsoft offering. These points concern the project’s posture, not whether the underlying ideas of provenance, temporal modeling, or conflict evaluation are useful. Check the repository’s current status before making implementation decisions, since project status can change.
The original GraphRAG paper reports qualitative improvements in comprehensiveness and diversity for global sensemaking questions over datasets in the one-million-token range. That task-specific result is not evidence that GraphRAG is generally more accurate, nor does it replace evaluating how a system handles conflicts in its own corpus.
A practical operating rule
For every answer that could change over time or vary by scope, make the evidence traceable, keep competing claims separate, and test whether the system can retrieve and explain them. Resolve only what the sources and a documented policy justify; preserve the rest as visible uncertainty.
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