Structured Data
Knowledge graph entity
A node in Google's Knowledge Graph representing a real-world thing (person, place, organisation, work). Strong entity signals are prerequisite for Search Profile eligibility and consistent AI Overview attribution.
What it is
A knowledge graph entity is a node in Google's Knowledge Graph that represents a distinct real-world thing such as a person, place, organisation, or creative work. Each node carries attributes and typed relationships to other nodes.
Why it matters
Strong, consistent entity signals are a prerequisite for Search Profile eligibility and for stable, accurate attribution in AI Overviews, since answer engines prefer to cite resolved entities over ambiguous strings.
How it works
You strengthen an entity by publishing consistent identifiers and descriptions, linking authoritative references via sameAs, and reinforcing the same facts across structured data and corroborating sources.
When it applies
It applies whenever a brand, author, product, or organisation needs to be recognised as a single, unambiguous thing across search and generative surfaces.
Examples
- A Person node linked to Wikipedia, an author bio, and a LinkedIn profile via sameAs
- An Organisation node with a consistent legal name, logo, and founding date everywhere it appears
- A creative work resolved to one node despite multiple editions or titles
How it is measured
- Whether the entity resolves to a single canonical node rather than several
- Consistency of core attributes (name, type, identifiers) across sources
- Count and authority of corroborating sameAs references
- Frequency of correct attribution in AI-generated answers
Related terms in Structured Data
- Content provenanceContent provenance is verifiable information about where a piece of content came from, who created it and how it has been changed, carried with the file or published alongside it. The main open standard is C2PA, shown to people as Content Credentials, which uses cryptographic signing to record capture, edits and any AI involvement. It helps platforms, search systems and readers judge whether an image, video or audio clip is what it claims to be.
- Feed freshnessFeed freshness is how recently a product or data feed has been updated, and how closely the values in it match what is live on your site. A fresh feed reflects current prices, stock levels and product details within minutes or hours rather than days. Stale feeds cause disapprovals, mismatched listings and lost sales.
- JSON-LDThe recommended syntax for embedding Schema.org structured data on a page. Lightweight, decoupled from page HTML, and increasingly the format LLMs prefer when retrieving structured facts.
- Product feedA product feed is a structured file of product data, such as titles, descriptions, prices, availability, identifiers and image links, supplied to shopping channels and platforms. It gives retail surfaces a machine readable source of truth for what you sell. Feeds power Google Shopping listings, Meta catalogues, marketplaces and increasingly AI shopping answers.
- Retrieval-augmented generationA generation approach where an LLM pulls relevant documents at query time and uses them as the source for its answer. The pattern behind most enterprise AI search products and Perplexity-style answer engines.
- Schema.orgThe shared vocabulary for structured-data markup used by Google, Microsoft, and major search engines. As of June 2026, Schema.org publishes monthly aggregate adoption statistics by type.
Primary research · August 2026
How ChatGPT Shortlists Software Brands
An audit across 10 categories and 60 buying questions. I recorded what ChatGPT reads, throws away and links to when a buyer asks it which software to buy, and what that decides.
- 60
- Questions asked
- 10
- Software markets
- 2,680
- Results read
- 367
- Links shown
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