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How do you handle entity conflicts when two people share the same name?

Quick answer

You keep two same-named people separate by giving each a dedicated schema-marked entity home, establishing distinct contexts (industry, location, role) through authoritative third-party citations, and adding sameAs links that anchor each identity to its own set of canonical profiles. Where either is notable, a Wikipedia disambiguation page and a unique Wikidata ID reinforce the separation.

Two people sharing a name is a classic entity-resolution problem: search and AI engines will conflate the two identities unless they are given enough distinct context to keep them apart. The fix is to build separate, well-anchored entity infrastructure for each person so the systems can resolve each to a distinct subject rather than merging them.

Diagram showing how two people who share one name - both named 'Alex Morgan' - are kept apart as distinct entities.
Two people, one name: each is kept distinct by a dedicated schema-marked entity home, distinct context from authoritative citations (industry, location, role), and its own separate sameAs links anchoring that identity. Without this infrastructure (red inset) the engines collapse the two into one confused entity and misattribute each person's achievements.

How to keep two same-named identities separate

  1. Give each person a dedicated entity home. Build schema-marked owned properties for each individual, so each has its own Person schema and a canonical description. Person schema with unique identifiers is what lets you distinguish two distinct individuals to the engines.
  2. Establish distinct contexts. Use authoritative third-party citations to differentiate them by industry, location, role, and associated topics. Those contextual signals are how Google disambiguates same-named entities, so making each person’s context unmistakable is the core of the work.
  3. Anchor each identity with sameAs links. Add sameAs structured links from each entity home to that person’s own set of authoritative profiles. This explicitly tells the systems which references belong to which person, because engines will not assume a website, LinkedIn page, and press profile describe one identity unless the signals say so.
  4. Reinforce with Wikipedia and Wikidata where notable. Where one or both people are notable enough, Wikipedia’s disambiguation pages and distinct articles, paired with unique Wikidata identifiers, are among the strongest disambiguation signals the engines use.

The failure mode we diagnose

When entity infrastructure is weak, no clean schema, no sameAs anchoring, no disambiguation, the engines confuse or conflate the two: the people merge into one confused entity, or one person’s achievements get attributed to the other. We verify the fix by checking that the AI engines return the correct person, monitoring how they describe each identity across the major engines with AIQ.

Last reviewed: 20/05/2026

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