Part of Generative Engine Optimization
Entity SEO
Entity SEO establishes your brand, products, and people as recognized entities in the knowledge graphs that ground generative answers.
Overview
AI engines reason in entities, not strings. Two pages mentioning 'Apple' are distinguished by surrounding context — the company, the fruit, the record label. Entity SEO makes that context unambiguous: consistent NAP (name/address/phone), Organization JSON-LD with sameAs links to Wikipedia, Wikidata, LinkedIn, and Crunchbase, Person schema for authors, and a tightly-themed page set that reinforces the entity's primary topics.
Components
- sameAs links
External identity references
- Organization schema
Brand markup
- Person schema
Author markup
- Wikidata entry
Canonical entity record
Related entities
- Knowledge Graph — underlying structure
- E-E-A-T — trust signal
Key facts
- sameAs links should point only to authoritative profiles for the same entity.
- Inconsistent brand naming across pages dilutes entity confidence.
- Person schema can clarify authorship when it matches a visible, credible author profile.
How to apply Entity SEO
- 1
Review sameAs links
External identity references. Check the current implementation, correct factual or technical gaps, then verify the public page rather than relying on configuration alone.
- 2
Review Organization schema
Brand markup. Check the current implementation, correct factual or technical gaps, then verify the public page rather than relying on configuration alone.
- 3
Review Person schema
Author markup. Check the current implementation, correct factual or technical gaps, then verify the public page rather than relying on configuration alone.
- 4
Review Wikidata entry
Canonical entity record. Check the current implementation, correct factual or technical gaps, then verify the public page rather than relying on configuration alone.
Implementation checks
| Check | Pass condition | Evidence to keep |
|---|---|---|
| Accuracy | Every claim matches the visible page and current source material. | Source URL and review date |
| Delivery | Important content is present in the public server response. | Saved response or crawler test |
| Consistency | Names, URLs, dates, and definitions agree across related pages. | Template and entity audit |
| Measurement | A fixed test shows whether discovery or answer accuracy changed. | Baseline and follow-up results |
Common mistakes
- Treating Entity SEO as a one-time task instead of maintained site infrastructure.
- Publishing claims or markup that visitors cannot verify on the page.
- Changing several variables at once, then guessing which change affected visibility.
- Measuring a single AI answer instead of a fixed panel of repeatable prompts.
Questions about Entity SEO
- What is Entity SEO?
- AI engines reason in entities, not strings. Two pages mentioning 'Apple' are distinguished by surrounding context — the company, the fruit, the record label. Entity SEO makes that context unambiguous: consistent NAP (name/address/phone), Organization JSON-LD with sameAs links to Wikipedia, Wikidata, LinkedIn, and Crunchbase, Person schema for authors, and a tightly-themed page set that reinforces the entity's primary topics.
- Where should implementation start?
- Start with sameAs links, verify the public result, then work through the remaining components in order. Record a baseline before making changes so the follow-up is meaningful.
- How should success be measured?
- Track technical validity, crawler access, accurate extraction, and citation presence across a fixed set of relevant questions. A single answer or crawler visit is not a trend.