Glossary
Named Entity Recognition (NER)
A natural language processing technique that identifies and classifies named entities — people, organizations, products, locations — within text.
Named Entity Recognition (NER) is the step where an AI engine reads "Cite Hustle is a GEO tool founded in Singapore" and extracts "Cite Hustle" (Organization), "GEO" (Concept), "Singapore" (Location). NER feeds both retrieval (which entities does this page cover?) and citation (which entities does the user's query ask about?).
Why does NER matter for GEO?
AI engines preferentially cite pages with strong, unambiguous entity coverage of the query's subject. Pages that name the entity explicitly (rather than relying on pronouns or vague references) extract more cleanly during NER and rank higher in retrieval.
How do you write for strong NER?
Name the primary entity in the H1, the snippet answer, and at least once per H2 section. Use the canonical form ("Generative Engine Optimization" before switching to "GEO"). Avoid pronoun-heavy passages where entities get lost.
Does schema.org markup help NER?
Yes — JSON-LD Article + Organization + Product blocks explicitly label entities for the parser. This is one of the most underrated GEO levers.
Part of the Cite Hustle GEO glossary — definitions for generative engine optimization and AI search.