How do AI models handle disambiguation for people and companies with common names?

Disambiguation (the engine working out which of several same-named entities you mean) is where strong entity work pays off and weak entity work produces visible failure. AI engines handle common names (a company that shares a name with another company, an executive who shares a name with a public figure) by relying on entity infrastructure: Wikipedia disambiguation pages that explicitly list the different entities, Wikidata IDs that anchor each entity uniquely, schema markup (tags in a page’s code that tell search engines what the content is) with sameAs properties (identifiers that tell engines a brand’s profiles are the same entity) that link a brand’s owned pages to its canonical entity identifiers, and contextual cues in the user’s prompt. When this infrastructure is in place, the engines route correctly. When it is weak (no Wikidata entry, no schema markup, no clean disambiguation), the engines guess, and the guesses can be wrong in damaging ways: an executive’s biography conflated with someone of the same name, a brand confused with an unrelated company in another industry. The fix is at the entity layer, not the prompt layer.