Search the name ‘Gilad Sasson’ and the index returns at least four people: an SEO consultant, a rabbinic scholar, a chemical-engineering PhD, and an eleven-year-old footballer. To a vector model, we occupy overlapping regions of space. Ask it for a biography and it will average us — confidently, fluently, and wrongly.
This is the entity-collision problem, and it is the single most underrated risk in AI visibility. Hallucination is not random; it is the predictable result of insufficient disambiguation. The model fills the gap with the nearest plausible neighbour.
The fix is not to write better prose. It is to provide machine-readable separation: a Person schema with a stable identifier, a consistent appositive (‘also known as Algoholic’), a sameAs array that pins the entity to verified profiles, and — eventually — a Wikidata node. Editorial cannot solve a structural problem. Structure can.
