How to Architect JSON-LD Entity Graphs for AI Search & LLM Citation (GEO)

Entity SEO for AI search runs on 3 layers: a canonical Entity Home, cross-platform sameAs corroboration, and attribute-rich service and local entity mapping. This post shows the JSON-LD behind each layer, as we apply it at Farrukh Abdullah Advisory, where AI SEO Consulting & Visibility Measurement restructures websites into verifiable knowledge graphs.

Entity SEO differs from keyword SEO in its unit of optimization: the entity (an organization, person, service, or place) rather than the keyword string. Google AI Overviews, ChatGPT Search, and Perplexity each compose answers from retrieved sources, so a brand with no unambiguous entity markup gives those systems less to verify. JSON-LD is a disambiguation signal, not a ranking switch.

Layer 1 turns that principle into markup by giving each entity one permanent address.

An Entity Home is the single page on your domain that holds the canonical record of one entity, identified in JSON-LD by a persistent @id. Assign each entity one @id as a URI fragment, such as https://yourdomain.com/#organization and https://yourdomain.com/#person, and reference that @id from every service page and article instead of redeclaring the entity.

Markup without an @id creates an anonymous node, so two pages that each declare an Organization produce two unconnected Organizations instead of one shared one. A service page that references the Entity Home's @id joins the existing node: