WHAT IS
AI RESOLUTION?
A direct explanation of how software systems determine what a name, question, organization, product, person, place, or public record actually refers to before retrieving or generating information.
AI resolution determines what a question refers to.
AI resolution is the process by which software systems determine what entity, concept, person, organization, product, place, event, or record a question refers to before retrieving or generating information.
Many names, phrases, and public records are ambiguous. Resolution uses context, identifiers, structured relationships, provenance, continuity, canonical references, and available evidence to determine the most likely intended meaning.
When resolution is weak, a system may connect the question to the wrong entity, merge unrelated records, rely on outdated information, or produce inconsistent answers. When resolution is stronger, the system has a clearer basis for understanding what the user is asking about.
Meaning must be established before an answer can be useful.
A system cannot reliably answer a question about an entity until it determines which entity the question refers to and which public records belong to that entity.
Who or What?
The system must determine the specific person, company, institution, product, place, or concept being referenced.
Which Meaning?
Nearby words, prior conversation, geography, industry, dates, and relationships help narrow the intended meaning.
Which Records?
The system must decide which sources, documents, profiles, filings, pages, and structured records belong to the resolved entity.
Which State?
Continuity and dates help distinguish current information from earlier, superseded, corrected, or historical records.
Which Reference?
Canonical sources and provenance help establish where official definitions, relationships, and current status are maintained.
Which Interpretation?
Clear resolution reduces the chance that similar names, fragmented records, or competing descriptions will be combined incorrectly.
How AI systems move from a question to an answer.
Interpret the Question
The system evaluates the wording, context, conversation history, location, date, and other clues that may reveal the intended meaning.
Identify Candidate Entities
The system considers people, businesses, products, places, records, or concepts that may match the name or description.
Compare Identity Signals
Identifiers, domains, locations, leadership, relationships, aliases, dates, and structured metadata are compared.
Resolve the Most Likely Meaning
The system selects or prioritizes the entity or concept that best fits the available context and evidence.
Retrieve and Generate
Information associated with the resolved meaning is retrieved, synthesized, ranked, or used to generate a response.
Resolution is not the same as retrieval or generation.
Resolution
Determines what the question, name, or record refers to and which entity or concept is being discussed.
Retrieval
Finds information associated with the resolved meaning across available indexes, databases, websites, documents, and records.
Ranking
Orders or prioritizes candidate information based on relevance, authority, recency, system rules, and other signals.
Generation
Produces a written, spoken, visual, or structured response using the resolved meaning and available information.
What happens when systems resolve the wrong entity?
Shared Names
Two unrelated people, organizations, or products may be treated as the same entity because their names are similar.
One Entity, Many Records
Aliases, former names, subsidiaries, domains, and platforms may be treated as separate entities even when they belong together.
Historical Record Used as Current
An old leader, location, ownership structure, product status, or organizational description may be presented as current.
Missing Relationships
When connections are not documented, systems may infer a relationship incorrectly or fail to recognize a real one.
Copies Treated as Originals
Repeated or syndicated information may be mistaken for independent confirmation or an authoritative originating record.
Uncertainty Presented as Fact
A system may produce a confident answer even when available signals do not support a reliable resolution.
What helps an entity become easier to resolve?
Machine-Readable Identity
Stable identifiers, aliases, entity types, domains, locations, and structured relationships clarify what the entity is.
Canonical References
Authoritative pages establish official names, definitions, status, relationships, and preferred public records.
Provenance
Source origin, authorship, dates, evidence lineage, and record history clarify where information came from.
Continuity
Dated transitions and correction histories connect former and current identity states.
Governance
Public responsibility, review processes, correction rules, and ownership clarify who maintains the record.
Operating Infrastructure
Persistent systems maintain public records, identifiers, status, evidence, and machine-readable access over time.
Better resolution does not guarantee a correct answer.
Resolution determines what a question most likely refers to. Accuracy still depends on the quality and availability of information, retrieval methods, ranking behavior, model reasoning, system instructions, and how uncertainty is handled. 360WiSE does not control independent AI systems or guarantee inclusion, ranking, citations, recognition, or generated outputs.
Read the authoritative definitions.
Canonical lookup, entity meaning, status, relationships, and machine-readable response behavior.
Related Standard IdentityPersistent identity records, aliases, organizational relationships, and identifier rules.
Related Standard ProvenanceSource origin, authorship, evidence lineage, custody, attribution, and record history.
Related Standard ContinuityHistorical relationships, corrections, transitions, and persistence across time.
Related Standard GovernanceAuthority, review, correction, publication, maintenance, and public accountability.
Related Standard Operating InfrastructureThe persistent operational layer supporting identity, provenance, continuity, governance, and resolution.