Track freshness by separating three questions: when a fact applies in the world, when your system recorded it, and when it is due for review. Store those times separately, preserve prior assertions when history matters, and apply temporal and verification filters before a retrieved fact reaches answer synthesis. A review deadline is an operational reminder—not proof that a fact has expired.
What “freshness” means in a Graph RAG system
A claim can be old but still true, newly ingested but already outdated, or valid for a past period but false today. One timestamp cannot represent all of those cases. Model the fact’s real-world validity separately from when your pipeline learned it and from the schedule for checking it again.
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- Validity time: when the assertion applies in the modeled world. Represent it with
valid_fromandvalid_to. An unknown date should remain unknown rather than being silently replaced with the ingestion time. - System time: when the pipeline observed or recorded the assertion, such as
observed_atorrecorded_at. Keep source publication or update time separate; a crawl date is not a substitute for either. - Review time: when policy says to check the assertion again, such as
recheck_afterorverification_due_at. Passing this deadline means “review needed,” not “the fact stopped being true.”
This distinction supports two different historical questions: “What was true on this date?” and “What did the system know on this date?” The first is about validity time; the second also requires system time.
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Represent a claim as an assertion with its own identity and provenance. Depending on the graph model, that can be a relationship with properties, a claim node, or a linked assertion node. A separate assertion is useful when multiple sources support the same relation but disagree about its value, dates, or scope: preserve the statements independently instead of flattening them into one unqualified edge.
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| Field | What it records | Example or rule |
|---|---|---|
fact_id |
Stable identity for this assertion | Keep identity distinct from the subject or relation, so a correction can be a new assertion. |
| Subject, predicate, value, scope | What is being asserted and under what context | Office A / has_manager / Person B / regional team. |
valid_from, valid_to |
When the assertion applies in the modeled world | For an illustrative interval, Office A’s regional team has manager Person B from 2024-01-01 until 2025-06-01. The end is exclusive in the example query below. |
observed_at or recorded_at |
When ingestion accepted the assertion | Record the system time independently of any date stated by the source. |
| Source identity and version | Which evidence supports the assertion and which version was processed | Store a source ID or locator, document version or content hash, and source publication or update time when available. |
recheck_after or verification_due_at |
When policy calls for another check | A due date prompts review; it does not establish a real-world end date. |
status, superseded_by |
Operational verification state and replacement link | Possible statuses include current, superseded, disputed, and needs_verification. Link a replaced assertion to its successor. |
| Provenance and processing metadata | How to trace and reproduce the extracted claim | Link the assertion to its source chunk and document; retain extraction or processing version metadata as needed. |
Microsoft GraphRAG’s documented model includes Documents, TextUnits, Entities, Relationships, Covariates, Communities, and Community Reports. Its documentation describes Covariates as claim information that can include time-bound statements, and TextUnits connect extracted knowledge to source text. This provides a provenance foundation, not a complete expiration or revalidation policy. Neo4j’s GraphRAG guidance likewise describes document-to-chunk and entity-to-originating-chunk links, which can help identify material to revisit when a source changes.
Handle source changes without erasing useful history
- Ingest with identity and provenance. Record the source ID, version or content hash, available source dates, system observation time, and links from document to chunks.
- Extract assertions with their evidence. Keep explicit dates from the source distinct from ingestion timestamps. If the source does not establish a validity date, leave it unknown rather than inferring one.
- Reprocess affected material when a source changes. Compare the new extracted claims with existing assertions. If a claim is corrected or replaced, add the new assertion and a supersession link. Close the older validity interval only when the evidence justifies an end date.
- Retain prior versions when historical or audit questions matter. Overwriting a value can make it impossible to answer what was true earlier or what the system previously recorded.
- Apply a review cadence by domain. Set shorter intervals for claims with high consequences or rapid change and longer ones for stable background information. There is no universal freshness TTL established by the GraphRAG and Neo4j documentation discussed here.
Neo4j’s time-based versioning guidance describes selecting graph elements by validFrom and validTo, including current-state and historical-time queries. It also notes the trade-offs: updates may require creating new versions, which can duplicate graph elements; more complex cases may call for combined modeling strategies depending on query needs and transaction frequency.
Filter for the question’s time frame before synthesis
Do not rely on the language model to infer that a result is stale from prose alone. Determine whether the user asks for the current state, a past state of the world, or what the system knew in the past. Apply those conditions during candidate retrieval, or guarantee that an equivalent filter runs before any candidate is used to construct the answer.
Current-state question
For a current answer, select assertions whose validity interval includes the chosen as-of time and whose verification state meets your policy. The following is an illustrative Neo4j-style pattern, not a built-in GraphRAG freshness feature. It uses an inclusive start and exclusive end for validity intervals:
MATCH (s)-[f:ASSERTS]->(o)
WHERE f.valid_from <= $as_of
AND (f.valid_to IS NULL OR $as_of < f.valid_to)
AND f.status = 'current'
RETURN s, f, oSet $as_of explicitly or define consistently how the application chooses it. A null valid_to means no known end date in this example; it does not prove the claim is permanently true. Decide how a due-but-unverified assertion should be treated: a stricter application may exclude it, while another may return it with a warning or request fresh evidence.
“What was true then?”
Use the requested historical date as the validity-time parameter and retrieve the assertion whose interval covers that date. Do not exclude it merely because a later assertion superseded it for current use. In the example above, a query for 2025-03-01 can select Person B; a query for 2025-07-01 should not select that assertion because its exclusive end is 2025-06-01.
“What did the system know then?”
In addition to the world-validity condition, restrict system time to assertions recorded by the requested knowledge date, for example recorded_at <= $known_as_of. If the application retains only the latest overwritten value, it cannot reliably answer this question. Neo4j’s Cypher manual supports temporal instants and durations as distinct temporal values and describes zoned times as UTC instants internally; the application still has to choose a clock and define what each stored timestamp means.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Pick a temporal design that fits the workload
| Design choice | Useful when | Trade-off to evaluate |
|---|---|---|
| Keep only the latest value | History and audit answers are not required. | Past states and prior system knowledge may be unrecoverable after an overwrite. |
| Version assertions with validity intervals | Users need current and as-of-date answers, or corrections must preserve history. | More versions can mean graph duplication and more update and query complexity. |
| Keep separate assertions per source | Several sources can independently support, disagree with, or update a relation. | Retrieval and synthesis need explicit conflict handling and provenance-aware ranking. |
These approaches can be combined. The right choice depends on whether the application needs snapshots, graph differences, temporal traversal, or an audit trail; how often records change; and how much complexity its queries can tolerate. Temporal GraphRAG research describes time-stamped relations that keep the same entity relation distinct across different times, a hierarchical time graph, and incremental updates. Treat that as a research approach, not a universal implementation prescription.
Set policy and monitor whether it works
A review interval is an application policy, not a number supplied by GraphRAG or Neo4j. Choose it using the claim’s change rate, consequence of error, source update cadence, and cost of checking. A due date should route an assertion to revalidation; it should not silently change the assertion’s validity interval or be treated as evidence that the claim is false.
Evaluate the pipeline with an evolving corpus, not only a static retrieval set. Include current-state questions, as-of-date questions, “what did the system know then?” questions, corrections, source deletion or retraction, conflicting sources, missing dates, and failed refreshes. Track stale-fact leakage into current answers, unresolved temporal conflicts, source-refresh failures, and use of superseded assertions.
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A June 2026 author-uploaded preprint by Neeraj Yadav reports 15–40% stale-fact errors for RAG across four evolving benchmarks and approximately 0% for its proposed MemStrata method in that evaluation. These are benchmark-specific preprint results, not a general production error rate or guarantee; the paper notes that its evolving benchmarks use structured templates and that extraction quality remains a limitation.
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