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Track freshness by separating three questions: when a claim was true in the world, when your pipeline recorded it, and when your system should check it again. Store those times with the claim’s source and version, preserve superseded assertions when history matters, and apply temporal and verification filters before the model synthesizes an answer. A recheck deadline is a reminder to verify—not proof that a fact expired.
What does “fresh” mean in a Graph RAG pipeline?
A claim can be old but still true, recently ingested but already false, or past its review deadline without being disproved. Treating these cases as one timestamp or a single TTL makes it difficult to answer both “What is true now?” and “What was true then?” reliably.
| Time or state | What it means | Example field |
|---|---|---|
| Valid time | When the assertion applies in the modeled world. It may be a bounded interval, an open-ended interval, or unknown if the source gives no date. | valid_from, valid_to |
| Observation or record time | When the pipeline observed or accepted this assertion. Keep it distinct from the source’s publication or update date. | observed_at or recorded_at |
| Revalidation deadline | When policy says to review the assertion again. Passing this date changes its verification state; it does not establish that the assertion stopped being true. | recheck_after or verification_due_at |
These are separate dimensions. For example, a policy value may have taken effect on 1 January, been published by its source on 20 December, and entered your graph on 22 December. If your team schedules another check for 1 March, that deadline is an operational decision—not the policy’s end date.
What should a claim record contain?
Model an assertion so it can be interpreted, traced to evidence, and revised without losing the earlier record. A practical schema can look like this; the values are illustrative, not claims about a real service:
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{
"fact_id": "plan-price-42",
"subject": "Example Plan",
"predicate": "monthly_price",
"value": "$24",
"scope": "US, monthly billing",
"source_id": "pricing-page",
"source_locator": "https://example.invalid/pricing",
"source_version": "sha256:…",
"source_published_at": "2026-02-15T12:00:00Z",
"observed_at": "2026-02-16T08:30:00Z",
"valid_from": "2026-02-01T00:00:00Z",
"valid_to": null,
"recheck_after": "2026-03-01T00:00:00Z",
"status": "current",
"superseded_by": null
}The example deliberately distinguishes the asserted effective date, source date, and ingestion time. Do not infer valid_from from crawl time when the source does not establish when the claim became true. Represent missing dates as unknown or null according to your schema, rather than silently inventing a start date. Preserve scope—such as geography, edition, customer segment, or billing term—so assertions that look contradictory are not merged when they actually describe different cases.
Keep provenance attached to the assertion
Store a stable source identifier and locator, plus a document version or content hash when available. Link each assertion to the source document and the relevant chunk, and retain extraction or processing version metadata needed to reproduce how the assertion was created. If two sources support different values, keep their assertions separate with their own provenance and intervals instead of collapsing them into one unqualified edge.
The right placement depends on the graph and whether multiple sources can independently support the same relation. Fields may live on a relationship, a claim node, or a linked assertion node. The important property is that a retrieved value can be traced to the evidence that supports that particular value.
How should GraphRAG and a graph database represent time?
Use the GraphRAG data model for provenance, not as an assumed expiry policy
Microsoft GraphRAG describes Documents, TextUnits, Entities, Relationships, Covariates, Communities, and Community Reports. A Covariate represents extracted claim information and may contain time-bound statements; TextUnits connect extracted knowledge to source text. As the Microsoft GraphRAG Dataflow documentation puts it, “A TextUnit is a chunk of text used by our graph extraction techniques.” The workflow chunks documents, extracts entities, relationships and claims, builds communities and summaries, and uses indexed structures during queries. This is a useful foundation for tracing claims, but the documented workflow does not itself establish a complete freshness, expiration, or revalidation policy.
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Choose versioning according to time semantics and history needs
Neo4j’s time-based versioning guidance describes storing validFrom and validTo on graph elements and selecting elements valid at a particular point in time. Its Cypher manual supports temporal values as node or relationship properties and distinguishes temporal instants from durations; zoned time values are stored internally as UTC instants. Your application still has to define what each field means, use a consistent clock, and choose how intervals are compared.
Versioning has trade-offs. Creating a new element when an assertion changes can retain history, but may duplicate graph data and make updates more complex. Neo4j’s guidance discusses choosing among time-based approaches in light of snapshots, graph differences, temporal traversal, query patterns, and transaction frequency. A simpler current-state representation may suit systems that only need the latest value, but it is insufficient if users need an audit trail or answers as of a past date.
Neo4j’s GraphRAG guidance also describes linking documents to chunks and entities to originating chunks, with building, enriching, and updating the graph as data-processing work. When a source changes, these links help identify which extracted material may need reprocessing. They do not, by themselves, decide whether an old assertion remains valid.
How do you ingest updates without erasing useful history?
- Record the source version. Ingest stable source identity, a locator, content hash or version, available source dates, and document-to-chunk links. Keep source publication or update time separate from the time your system observed it.
- Extract assertions with evidence links. Capture claim scope and any dates stated by the source. Connect each extracted assertion to its supporting chunk and document.
- Compare claims when a source changes. Reprocess affected chunks and compare the resulting assertions. If a new assertion replaces an old one, add a new version and a
superseded_bylink. Close the old validity interval only when the evidence justifies an end date; retain the old assertion and its provenance when historical or audit questions matter. - Set a review policy by claim type. Choose revalidation intervals based on how quickly the information changes and the consequence of getting it wrong. A fast-changing, high-impact claim may deserve earlier review than stable background information. There is no universal TTL duration established by the GraphRAG and Neo4j documentation discussed here.
- Handle unresolved cases explicitly. Mark assertions as disputed or needing verification when evidence conflicts, a refresh fails, a source disappears, or dates are missing. Do not silently treat a failed refresh as confirmation that the old value remains current.
How should retrieval answer current and historical questions?
Determine the temporal frame before ranking candidates. “What is true now?”, “What was true on 15 January?”, and “What did the system know on 15 January?” are different questions. The first two concern valid time; the third also constrains record time. Filter during candidate retrieval or guarantee that temporal and verification filters are applied before synthesis, so an out-of-date candidate cannot slip into context merely because it ranks highly.
Illustrative query patterns
- Current-state question: select assertions whose validity interval includes the present query time and whose status is eligible under your verification policy. An assertion past
recheck_aftermay require a needs-verification state or a stricter policy; the deadline alone does not prove it false. - As-of-date question: select the assertion whose validity interval includes the requested date, even if it has since been superseded. Do not exclude it just because it is no longer current.
- What-was-known-then question: select evidence valid for the requested world-time frame and recorded by the requested system-time cutoff. This avoids using a correction ingested later to describe what the pipeline knew earlier.
Pass the selected assertion’s value, scope, validity interval, verification state, and source reference into model context. The answer should cite the source supporting the selected assertion, not merely name a graph node. Include uncertainty where dates are unknown, evidence conflicts, or a review is overdue.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can you test freshness and expiration behavior?
Static-corpus retrieval scores do not show whether a system handles change correctly. Build tests around an evolving corpus and check both retrieval and the final answer.
- Current-state questions after a source changes, including whether a superseded value leaks into the answer.
- Explicit as-of-date questions that must retrieve an older assertion whose interval covers that date.
- “What did the system know then?” questions where a later correction must not appear in an earlier system-time view.
- Corrections, source deletions or retractions, conflicting sources, and assertions whose dates are missing.
- Failed refreshes, overdue checks, and the transition from verified to needs-verification status.
- Provenance checks confirming that each answer points to the source and chunk supporting its selected claim.
Track stale-fact leakage, unresolved temporal conflicts, failed refreshes, and answers that rely on superseded assertions. Compare candidate designs by history retention, update or rebuild cost, query complexity and latency, provenance completeness, conflict handling, and risk of old versions appearing in current answers.
Temporal GraphRAG research proposes keeping relations from different times as distinct edges, with a hierarchical time graph and incremental updates. It is a research approach, not a universal implementation prescription. A June 2026 author-uploaded preprint by Neeraj Yadav reported 15–40% stale-fact errors for RAG and approximately 0% for its proposed MemStrata method across four evolving benchmarks. Those are results reported for that evaluation, not expected production rates or independent consensus; the paper notes that its evolving benchmarks use structured templates and that extraction quality remains a limitation.
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