Entity Map

Why an entity map

Knowledge graphs are how Google understands entities and their relationships — who someone is, what they’ve built, how concepts relate. For a personal site, declaring these relationships explicitly helps search engines and AI systems build an accurate picture without having to infer everything from prose.

The EntityMap v1.0 spec provides a structured format for self-declaring entities: their type, description, sameAs links to authoritative sources, relations to other entities, and hasChunks — actual text extracts that serve as grounding data for LLMs.

The 8 entities

People:

• e_001 Lee Driggers (Person) — SEO professional, trail runner. sameAs LinkedIn.

Organizations:

• e_002 leedriggers.com (Organization) — this site. sameAs the canonical URL.

Concepts:

• e_003 Crawl Intelligence — the D1 + KV bot monitoring pipeline
• e_004 Edge SEO Architecture — running SEO logic at the CDN edge
• e_005 Real User Monitoring — CWV measurement pipeline. sameAs Wikidata.
• e_006 Bot Classification — the UA-string classifier
• e_007 Technical SEO — the broader discipline. sameAs Wikipedia.
• e_008 Ultra Running — trail and mountain running. sameAs Wikipedia.

Relations

Relations use explicit predicates: AFFILIATED_WITH, AUTHORED_BY, INCLUDES, DEPENDS_ON, RELATES_TO, PART_OF, PRODUCED_BY. These model the actual structure:

• Lee Driggers AFFILIATED_WITH leedriggers.com, AUTHORED_BY Crawl Intelligence + Edge SEO Architecture
• Crawl Intelligence INCLUDES Edge SEO Architecture + Bot Classification
• Technical SEO INCLUDES Crawl Intelligence + Real User Monitoring
• Ultra Running AFFILIATED_WITH Lee Driggers

hasChunks — grounding data for LLMs

Each entity includes hasChunks: short text extracts that provide factual grounding. These are structured like retrieved documents: chunkId, text, sourceUrl, pageTitle, relevanceScore, contentType (definition, evidence, example).

The intention is that an LLM processing the entity map has ready-made factual summaries per entity, linked back to their source pages, rather than having to extract meaning from prose.

Two surfaces: JSON and human-readable

/entitymap.json — machine-readable, cached for 24 hours, tracked in the crawl intelligence pipeline alongside other AI-facing endpoints.

/entitymap/ — human-readable page with entity cards, type badges, and relation chips. Lets humans see the same data without parsing JSON.

What I’d add next

• Blog posts as hasChunks evidence (link specific posts to the concepts they document)
• Employer organizations as entities (Bankrate, HomeLight, Chegg, BrightEdge) with employment relations
• Expanding sameAs links for the Concept entities as relevant Wikidata entries exist