When two AI-retrieved sources disagree, the safest answer is rarely ‘choose the newest page’ or ‘take the majority’. The assistant must check whether the claims concern the same person, programme, date, location, units and decision; whether one source genuinely supersedes another; and who has authority to establish the particular fact. A difference in scope is not a contradiction. An unresolved same-scope contradiction may require a qualified answer or deliberate abstention.
The practical method is to create claim-specific evidence records, match the context, inspect source provenance, apply effective-time and authority rules, and verify consequential outcomes independently. This chapter works through forty-eight fictional flagged discrepancies, distinguishes revisions from genuine conflicts, compares invented workshop notices and transfers the method to educational course requirements. No fictional numbers are presented as measured eduKate results, and no discussion assumes artificial superintelligence already exists.
For the broader learning journey, begin at How Super Intelligence Works. The grounding chapter teaches how individual claims connect to evidence, while the data consistency chapter explains replicas and authoritative records. This guide concerns the additional reasoning needed when sources make apparently incompatible assertions.
Choose your reading route
- For parents or students: compare time, audience and current source before acting on a course or event notice.
- For researchers: examine provenance, claim scope, source version, units and evidence spans.
- For technical builders: implement the comparison key, abstention state and reviewable source ledger.
- For auditors: solve the forty-eight-claim classification and the course-entry transfer exercise.
Contents
- 1. Recognise a real disagreement before trying to settle it
- 2. Model claims, documents, authors and revisions separately
- 3. Separate authority from popularity and proximity
- 4. Build a comparison key for each question
- 5. Use a five-step conflict decision rule
- 6. Distinguish source contradiction from execution disagreement
- 7. A fictional forty-eight-claim review
- 8. Use a claim comparison table without a fake voting score
- 9. Resolve time and scope before blending values
- 10. Choose abstention without becoming unhelpful
- 11. Keep provenance evidence separate from model confidence
- 12. Detect and repair systematic conflict errors
- 13. A worked transfer case: course entry requirements
- 14. Practical classroom and professional exercises
- 15. Limits and responsible next steps
- Frequently asked questions
- Sources and standards
- Continue exploring
1. Recognise a real disagreement before trying to settle it
Two documents are not in conflict merely because they contain different words. One may describe a historical situation while another describes the current one; one may govern a different school, applicant category, user account or location. Before an AI assistant claims that sources contradict, translate each relevant sentence into a specific claim with a subject, predicate, value, scope and time. Only then decide whether both claims can be true simultaneously. A fluent answer that skips these steps can make a change of context look like a crisis.
Consider two fictional event notices. A Monday flyer says a library workshop starts at 08:00, while a Thursday update says 09:00. If the Thursday notice explicitly replaces the Monday timetable for the same workshop and date, the records represent successive versions, not two equally current rules. The older flyer remains valid evidence of what was communicated earlier. It should not govern the student’s present travel decision after the change is verified. Source freshness is part of meaning, not a separate cosmetic label.
Now consider a subtler case. A parent asks when an introductory workshop starts. The school’s page says 08:00 for staff, while the public registration page says 09:00 for families. Both could be correct for their intended audiences. The resolver must identify the user’s role and exact question rather than select whichever number appears on more pages. A generic system that announces ‘the start time is 08:00’ has confused scope with credibility.
A third situation is a genuine unresolved conflict. Two equally scoped, current pages issued by the same authority show incompatible closing dates, and neither states that it supersedes the other. The safe action is to expose the discrepancy, avoid a false guarantee, and seek the accountable owner’s clarification before a consequential decision. Abstention is not a failure to understand language; it can be the correct inference from insufficient evidence.
Return to contents or proceed to Model claims, documents, authors and revisions separately.
2. Model claims, documents, authors and revisions separately
Build a claim record that can survive beyond one model response. A useful record identifies the proposition being evaluated, exactly what was said, the relevant entity or programme, applicant or audience scope, source artifact ID, publisher, publication date if known, effective interval, evidence span and retrieval timestamp. A source URL by itself is not enough. It may change content, disappear or point to a page containing several unrelated policies. The claim record should explain which words support which conclusion.
Provenance is the history of origin and transformation, not proof of truth. The World Wide Web Consortium’s PROV vocabulary describes entities, activities, agents and derivation relationships. That can help express how an updated policy summary was produced from an institution’s notice, and who participated in the process. It does not certify that the summary is accurate or that the institution remains the current authority. Verification must inspect the actual claim and context.
Give revisions explicit identities. A school notice published in September and amended in October should not be silently merged into one timeless source. An assistant answering a question about the September rule may legitimately cite the earlier revision, while a new application decision should use the current applicable instruction. Preserve both with the recorded relationship, not a brittle overwrite that destroys the historical evidence. The present decision and the audit of a past decision have different jobs.
Distinguish observation time from effective time. A crawler might retrieve a page on Friday that announces a rule effective next Monday. Today it is an official future notice, not necessarily the currently operating rule. Another page may be recorded today but describe a past examination cohort. Human readers usually infer this from prose; robust assistants should represent it explicitly in claim metadata and check it against the user’s intended date.
Return to contents or proceed to Separate authority from popularity and proximity.
3. Separate authority from popularity and proximity
An officially responsible institution may be the appropriate authority for its own admissions requirements, while a third-party education blog can be useful for explaining concepts without owning the final policy. A dozen copied snippets can all derive from one obsolete source. Counting snippets as independent votes creates an illusion of consensus. The resolver should trace whether they share an original, and give the original responsible issuer appropriate weight for the specific question.
Authority is scoped, not universal. A university can specify its programme requirements but cannot automatically determine a country’s immigration approval. A school may confirm its published activities but cannot settle another school’s enrolment rules. A payment provider can confirm an accepted transaction ID but not whether the payer authorised the agent to initiate that transaction. The person or organisation that knows one fact is not necessarily authoritative for the next fact.
Do not turn source ranking into a single universal score. A legal statute, an institution’s official policy, a live tool-state response, a timestamped personal authorisation and an independent observation have different roles. A recent blog post may be newer than an official rule without replacing it. A current policy page may be authoritative but not relevant to the particular cohort. A correct resolver applies a decision-specific precedence contract and then checks for exceptions and supersession.
Popularity signals can provide leads for further checking, never automatic truth. Many people repeating an answer might indicate that a source was widely read, not that it was correctly interpreted. Even an accurate model paraphrase can omit a condition in a table footnote. Require an evidence span supporting the precise conclusion: not just a citation label, not the domain’s reputation alone, and not the model’s confidence about its own words.
Return to contents or proceed to Build a comparison key for each question.
4. Build a comparison key for each question
The comparison key is the set of attributes that must match before two claims can be treated as direct rivals. For a fictional programme, include institution, programme code, qualification, applicant category, intake cycle, policy effective date and decision type. For a hardware device, use exact model, firmware revision, environment and measurement method. For a laboratory result, use sample identity, units, procedure and timestamp. A conflict resolver does not need a complicated ontology to begin; it needs the right distinctions.
Imagine a parent asks about a course’s minimum eligibility, while one web result gives the previous year’s admitted-student profile. These are not competing answers to one question. Minimum eligibility and competitive selection describe different properties. A responsible article should explain the difference, identify the exact current official source and avoid inferring a guaranteed offer from historical data. The assistant must not resolve the apparent conflict by choosing a more attractive number.
Units matter as much as dates. An instrument reports 35 milliseconds while a dashboard shows 0.04 seconds, and a careless comparison claims a contradiction. Convert before adjudicating: 0.04 seconds is 40 milliseconds, so the difference is 5 milliseconds, possibly attributable to rounding or distinct measurements. The records could still conflict if they claim the same measurement under exact conditions; the unit conversion simply reveals the actual question.
Audience labels can prevent harmful errors. ‘Staff access at 07:30’ is not necessarily public admission at 07:30. ‘Secondary 3 elective’ does not imply an offering for Primary 5. ‘Scheduled’ does not imply ‘approved’. A model should ask for a missing decisive scope attribute only when necessary, and otherwise qualify its answer. That is more respectful to users than presenting a false precision based on whichever sentence was retrieved first.
Return to contents or proceed to Use a five-step conflict decision rule.
5. Use a five-step conflict decision rule
Step one is to establish the actual user decision. What will the person do with the answer, and what is the consequence of being wrong? A curiosity question about past timetables permits a different response from a request to travel for an appointment today. Step two is to identify the matching claim keys and filter obviously unrelated contexts. Do not decide which source wins until irrelevant comparisons have been removed.
Step three is to inspect provenance and original evidence. Who issued each record? Does the cited span say the proposed value? Is the document a current policy, a historical notice, a draft, a third-party explanation or a cached copy? Look for a verified revision or explicit supersession statement rather than inferring it from a filename. If an image or PDF has been reuploaded without an original date, that uncertainty must remain visible.
Step four is to apply effective-time and authority rules. A later official correction for the same programme and cohort can supersede an earlier one. A newer but unofficial post usually cannot. A live database read may govern whether a specific application was submitted, whereas an official policy governs eligibility. If material sources still disagree within identical scope and no authorised precedence rule settles it, record the unresolved conflict and route it to the owner.
Step five is to state the result with appropriate confidence, provenance and next action. An answer may be ‘the later official notice lists 09:00 for this session’ with its exact citation and date. It may instead be ‘the two current official sources conflict; I cannot responsibly confirm a time’. The resolver can suggest checking a named responsible office without inventing a phone number. It must never execute a consequential action from a disputed assumption.
Return to contents or proceed to Distinguish source contradiction from execution disagreement.
6. Distinguish source contradiction from execution disagreement
Evidence conflict is not limited to published pages. An assistant may report that a file is saved, while the storage API returns an accepted job state and a later independent read finds the old bytes. The model’s own message and the system observation disagree. In this case the authoritative state for storage completion is the actual saved resource or a reliable completion receipt, not the natural-language summary. The correct next step is reconciliation, not choosing the more optimistic sentence.
An agent’s approval record can also disagree with its proposed tool command. A user authorises ‘draft only’, but the tool arguments include a send destination. Both records can be authentic; they reveal an unauthorised proposal, not two equally valid preferences. The agent should hold the command and seek separate approval if the user really intends to send. Treating action arguments as implicit authority would erase the very boundary the system is supposed to protect.
Data consistency problems sometimes masquerade as epistemic disagreement. Two replicas may show different versions of the same business record because an update has not propagated. The correct response depends on the datastore’s read guarantees and revision identifiers. Once the authoritative source and expected version are established, a stale replica is a systems problem rather than a philosophical debate over evidence. The data consistency guide develops those mechanics separately.
Yet even a strongly consistent record can be factually wrong. A database may have one current value because an authorised operator made an error. A citation may accurately reproduce that value without making it correct. The system should allow verified correction, preserve the original state for audit and avoid conferring truth merely because the data is stored in a trusted backend. Business authority and factual accuracy are related but not interchangeable.
Return to contents or proceed to A fictional forty-eight-claim review.
7. A fictional forty-eight-claim review
Use the imaginary Orchard Learning Calendar to practise classification. A reviewer examines forty-eight candidate claim disagreements produced by a search assistant. Nineteen are resolved by an explicit current revision that supersedes an earlier notice; twelve concern different effective periods and therefore are historical rather than direct conflicts; nine involve different audiences or programme scopes; five lack enough provenance to choose; and three remain genuinely contradictory after scope and version checks. The exclusive counts total 19 + 12 + 9 + 5 + 3 = 48.
Each category needs a different outcome. The nineteen revision cases should cite the newer applicable issuer statement while preserving the old version as history. The twelve temporal cases require a date-conditioned explanation. The nine scope cases should identify the right audience instead of pronouncing one page wrong. The five unverified-origin cases need better source tracing. The three actual contradictions require accountable clarification or a qualified abstention. Simply sending all forty-eight to a ‘choose the best answer’ model loses the diagnoses.
Notice how misleading a single conflict percentage would be. The raw set contains forty-eight suspected discrepancies, but only three of them remain direct unresolved contradictions under the exercise’s rules. The rate is 3 ÷ 48 = 6.25% of flagged candidates, not 6.25% of all answers issued by the imaginary service. There is no measurement of its total traffic, so we cannot infer an overall error rate. Five more items are unverified, which is a separate risk and must not be hidden inside the resolved category.
If the five provenance gaps are treated as unresolved pending evidence, then eight of forty-eight flagged candidates remain open in some form: 5 + 3 = 8, or 8 ÷ 48 ≈ 16.67%. That is an operational workload rate for this chosen review packet, not proof that 16.67% of the organisation’s knowledge is wrong. Define the label before calculating. The same records can support different honest statistics as long as the denominator and category are explicit.
Return to contents or proceed to Use a claim comparison table without a fake voting score.
8. Use a claim comparison table without a fake voting score
Imagine three invented sources for workshop arrival time. Document W-1, a flyer, shows 08:00 for attendees on the October 12 session. W-2, a correction issued by the same organiser, shows 09:00 for attendees on that same date and explicitly replaces W-1. W-3, a staff rota, says 07:30 for the setup team. The comparison key identifies W-1 and W-2 as revisions for the same audience, while W-3 is a different scope. The current attendee answer is 09:00 if W-2’s validity is verified.
Do not assign W-3 a lower numerical trust score merely because its time differs. Its purpose is to describe another group’s arrival, and it may be completely correct. Likewise, a crawler saving W-1 more recently than W-2 does not make W-1 the newer policy. Retrieval timestamp is not source issue time. A claim table should record these dimensions separately so that the resolution can be reviewed.
Build the table with columns for identifier, claim, effective audience or interval, provenance and disposition. Keep it readable on mobile by using succinct labels rather than stuffing complete source texts into every cell. For real cases, link to the exact authoritative materials and preserve a secure copy or hash where appropriate. Never embed private user records or access tokens in a public example. The fictional packet below demonstrates relationships, not actual appointments.
Now imagine W-2 did not mention supersession and a second equally authoritative current notice still said 08:00. The resolver could no longer conclude 09:00 merely by picking the later indexed page. It should record an unresolved equal-scope conflict and seek the organiser’s decision. The table’s source pedigree would make that uncertainty explainable rather than embarrassing.
| Record | Claim | Audience and relationship | Decision |
|---|---|---|---|
| W-1 | 08:00 | Attendees; older flyer | Superseded by W-2 |
| W-2 | 09:00 | Same attendees; explicit correction | Current if verified |
| W-3 | 07:30 | Staff setup | Different scope |
Return to contents or proceed to Resolve time and scope before blending values.
9. Resolve time and scope before blending values
Some numerical source disputes come from different reference periods. A report says twenty-five applications were received before noon and another says thirty by the end of the day. Both may be correct cumulative counts. A model that averages them to 27.5 applications has produced a meaningless quantity. State the interval, counting method and whether the figures are cumulative, point-in-time or estimates. An arithmetic operation is only legitimate after the measures are comparable.
Other disagreements arise from different denominators. A school’s information session may be 80% full in a room of fifty seats, while a separate registration portal says forty-five of sixty registration places are taken. The values describe distinct capacities and possibly distinct booking populations. Calculate explicitly: 0.8 × 50 = 40 occupied seats; 45 ÷ 60 = 75% registrations. Without knowing how seats and registrations correspond, neither percentage refutes the other.
A source can be current and incomplete. A notice might show the morning session but omit an afternoon option. The absence of a mention is not proof that the option does not exist. Use negative evidence carefully: an exhaustive official list can support a stronger absence claim than a summary page that was never intended to be comprehensive. State whether the source asserts a closed set or merely provides examples.
Temporal semantics helps when an approval or policy crosses a boundary. A source can be genuine when issued but ineffective for the queried date. Prefer explicit half-open intervals with known timezone conventions where suitable, and do not infer precise instants from ambiguous local strings. The temporal semantics chapter treats event time, effective time and expired authority in more detail.
Return to contents or proceed to Choose abstention without becoming unhelpful.
10. Choose abstention without becoming unhelpful
An assistant should abstain from a claim when material uncertainty cannot be resolved safely with the evidence and tools available. That is not the same as refusing to explain the situation. It can show the two candidate claims, explain why their scopes appear identical, identify missing evidence and state the responsible clarification path. The user can still make progress: they know what is not known and what to verify. This is especially valuable when a wrong answer would cause missed enrolment, privacy harm or an irreversible transaction.
A structured abstention record might contain the disputed proposition, candidate source IDs, precise conflicting spans, evaluation date, the failed precedence test, consequence level, an accountable owner and a deadline for rechecking. It should not invent a likely answer to fill the field. If the dispute is about a live account state, allow a fresh authorised system read rather than encouraging the user to trust a cached chatbot response.
Sometimes clarification can be made narrow. Ask which intake year the parent means, not for a long autobiography. Ask whether the question concerns staff setup or public entry, not for unnecessary personal details. The missing attribute should be one that could actually change the decision. A needless clarification can inconvenience users as much as an unsupported guess, so the resolver should use context that is already legitimately available.
Safe abstention also needs a path out. If an official correction arrives, update the claim record and mark which uncertainty it resolves. If a tool can verify the current operational state, run the necessary authorised check and record its result. If no route exists, explain the durable limitation rather than leaving an endless spinner. Human control is meaningful only when people can see the unresolved issue and decide what to do next.
Return to contents or proceed to Keep provenance evidence separate from model confidence.
11. Keep provenance evidence separate from model confidence
A language model may express strong confidence in a fluent reconciliation, but confidence is not a guarantee of correctness and may not be calibrated for this task. The correct evidence is the source text, its scope, its authority and the independently observed state. Treat model-generated rationale as an explanation proposal that must be checked. A source can be primary yet no longer current, and a later secondary analysis can correctly describe that fact. Neither category can be reduced to one number.
Provenance graphs can help reveal copying. If five websites quote the same older event notice, a derivation graph can show that they share a single underlying statement. The number of links is not independent confirmation. The W3C PROV model gives vocabulary for agents, entities and derivations; the application must still assess actual authenticity and truth. A convincing-looking provenance diagram built from unverified relationships is not a certificate of reliability.
Cryptographic hashes can help preserve an exact document version, but a hash alone cannot prove that the issuer had authority, that the content was true or that it applies to the user’s case. Digital signatures can establish an authenticated link to a signing key under a particular trust arrangement; they do not automatically settle interpretation or policy effectiveness. Source evaluation remains a socio-technical process, not a contest to collect more sophisticated metadata.
One useful diagnostic is to have a different reviewer reconstruct the answer using only the recorded evidence packet, with the model’s final prose hidden. Can the reviewer identify the applicable source and explanation? If not, the evidence chain is insufficient or the decision is not reproducible. This independent check is stronger than asking the same model whether it is certain. It also exposes where the interface concealed a caveat or footnote from the original document.
Return to contents or proceed to Detect and repair systematic conflict errors.
12. Detect and repair systematic conflict errors
Failure one is inappropriate averaging. Two official documents list different eligibility ages because they apply to different programme tracks. An AI averages the ages and invents a criterion that no institution issued. Repair by separating track scope and comparing only matching claims. An average is appropriate for a numerical measurement with compatible units and definitions, not for two distinct policies.
Failure two is treating a copied old snippet as a fresh independent source. A search engine indexes multiple mirrors of a superseded notice, and the assistant thinks the older figure has majority support. Repair by identifying the original publisher, issue date, revision chain and authoritative current notice. Consider whether the copied pages are labelled archival. Popularity and duplication do not make a source current.
Failure three is losing the basis for an answer after a content refresh. The system overwrites a cached page while retaining the generated conclusion, so a later reviewer cannot verify what the model actually saw. Preserve source revision IDs or an appropriate evidence snapshot and maintain a revision relationship. Do not pretend the later page supports the old answer when its relevant passage has changed.
Failure four is an approval dispute disguised as a source conflict. The assistant says sending is allowed because a workflow template mentions email delivery, while the actual user approval permits drafting only. The user-specific authority governs the action, and the template cannot override it. Pause transmission, show the proposed recipient and content for explicit approval, and verify the actual result if permission is granted.
Failure five is conflating data freshness with correctness. A new tool response arrives later than an older independent record, but its revision is lower because it came from a delayed replica. Compare authoritative revision IDs and consistency semantics, not wall-clock retrieval time alone. A later read from the wrong endpoint can be less current than an earlier read from the primary system.
Return to contents or proceed to A worked transfer case: course entry requirements.
13. A worked transfer case: course entry requirements
Transfer the method to an invented training institute with two applicant pathways. Pathway A requires a fictional mathematics preparation module for applicants without a specified prerequisite. Pathway B waives that module for applicants who have already completed a recognised equivalent. An old general flyer says ‘every applicant takes the module’, while a current programme-specific page lists the waiver. The first task is to determine whether the wording conflicts for the same programme, category and effective intake, not to guess which route the user has.
Assume the institute confirms that the programme-specific waiver page is the operative rule for the new intake and that the old flyer is archival. Then the present answer should state the conditional rule and link the current responsible source. The assistant cannot promise the individual qualifies without verifying the accepted credential and applicant category. A correct source precedence decision is necessary but not sufficient for a personal eligibility judgement.
Now change the facts: the current general handbook and current programme page disagree, with no documented exception, no revision relationship and equal institutional authority. What should the agent do? It should preserve both source spans, identify the precisely conflicting requirement, abstain from a guaranteed eligibility claim and route the ambiguity to the institute’s responsible admissions office. It may still explain how to prepare a question and which details the applicant should verify.
Calculate a small operational measure. Suppose a reviewer finds eight equal-scope contradictions among sixty flagged cases after source matching. That is 8 ÷ 60 ≈ 13.33% of flagged cases, not thirteen percent of every institute policy or application. If two of the eight are resolved by a newly issued correction, six remain open, or 6 ÷ 60 = 10% of the flagged packet. Distinguish the reporting population from real applicant outcomes. This reasoning transfers to university entry, school timetables and workplace instructions.
Return to contents or proceed to Practical classroom and professional exercises.
14. Practical classroom and professional exercises
A student can build a claim-comparison notebook using invented event posters. Include a dated original, a dated correction, a notice for staff, a notice for attendees and an undated social repost. Ask the student to group claims by event and audience, identify which source governs today’s question and explain what further evidence would change the result. The aim is not to memorise a ranking such as ‘newest wins’, but to justify each comparison key.
A teacher can change one property at a time. Keep content identical but change the effective year; keep the date but change the audience; keep the source name but remove evidence of authenticity. Every change should trigger a concrete reevaluation. This teaches transfer: the same words do not have the same meaning in every context, and a correct answer for one person or date may be wrong for another.
A builder can implement a claims ledger with typed attributes and run acceptance tests against it. Supply a false supersession marker, an outdated replica, a missing authorisation and two genuinely equal-scope conflicting notices. Check that the resolver can return current, historical, different scope, unverifiable or unresolved conflict instead of inventing a single universal winner. Protect real personal data and use a sandbox for test actions.
A manager can make the decision contract auditable. Specify which official or technical owner can clarify an unresolved claim, how often time-sensitive sources should be revisited, how corrections reach affected users and what triggers suspension of a consequential automated action. Document the work rather than relying on a chat model to remember its own promises. The evidence process should survive staff turnover and software upgrades.
Return to contents or proceed to Limits and responsible next steps.
15. Limits and responsible next steps
No list of evidence rules eliminates uncertainty. A current official notice can contain an error; a highly reputable organisation can publish a draft inadvertently; two databases can briefly disagree because of replication; a photograph can omit context; and a human may misidentify the relevant cohort. Good systems respond by representing uncertainty, preserving evidence and offering a path to correction. They do not create a myth of infallible sources.
The appropriate burden of proof depends on consequence. An approximate historical fact in a casual discussion may tolerate a short qualification. A scheduled deadline, payment amount, permission to send or eligibility decision needs source-specific verification and sometimes accountable professional review. Do not confuse the model’s helpfulness with power to make institutional decisions. Where the source is not sufficient, the system should stop short of an actionable guarantee.
Provenance should be proportional. Building a complex graph for every harmless word choice can overwhelm users and teams. Focus detailed claim chains on information whose currency, scope or authority changes a decision. For routine explanations, a few accurately scoped sources and visible caveats may suffice. For irreversible action, stronger logged checks and current permission are warranted. The goal is reliable decisions, not maximal metadata for its own sake.
The transferable lesson is simple to say and difficult to practise: compare like with like, inspect who issued what and when, do not treat repetition as independent confirmation, and refuse to promote uncertainty into a confident instruction. A reader who can apply this method to an event timetable can carry it into scientific findings, AI-generated reports, school transitions and professional tool workflows. The final measure is whether the person receives information they can safely use.
Return to contents.
Frequently asked questions
Does the newest document always win?
No. It must apply to the same scope and come from the appropriate authority. A newer unrelated page or unofficial repost cannot automatically replace a current official rule.
Does an official source guarantee factual correctness?
No. Authority helps identify who owns a rule or record, but authentic sources can be mistaken, incomplete or superseded. Verify the exact claim and effective context.
Can a model vote among contradictory sources?
Counting copies is unreliable when sources share one origin or apply to different audiences. Compare authority, provenance, dates and scope before drawing a conclusion.
When should an AI agent abstain?
When material equal-scope conflicts remain unresolved or necessary provenance is missing and a wrong answer could mislead or trigger harm. Explain the uncertainty and the next verification route.
Does a provenance graph prove the conclusion is true?
No. It records claimed origin and derivations. Truth requires relevant primary evidence, correct interpretation and accountable verification.
How do readers check a numerical conflict?
Reconcile units, populations, time periods, definitions, measurement methods and denominators before deciding whether the figures genuinely disagree.
Sources and standards
Sources reviewed 23 October 2026. References support vocabulary and methods rather than certifying the fictional Orchard Learning Calendar, invented workshop times or exercise outcomes.
- W3C PROV-O: Formal vocabulary for provenance relationships among entities, activities and agents.
- W3C PROV Data Model: Distinguishing revisions, derivations, attribution and original entities.
- NIST AI RMF Playbook — Measure: Evidence-based assessment, uncertainty and human review in an AI system context.
- NIST AI Risk Management Framework overview: Voluntary risk-management principles, not source-by-source truth certification.
- HTTP Semantics (RFC 9110): Background on HTTP representations, validators and why a retrieval event alone is not a source authority judgement.
Continue exploring
Read Grounding, Data Consistency, Temporal Semantics, Evidence Ledgers, the How X Works library and the Gold Standard of Education.