Entity Disambiguation With Schema.org and Wikidata
How accurate structured data can describe an organization consistently across its own pages.
The Brand Disambiguation Problem
Consider a company named "Linear". In pure linguistic space, "linear" refers to algebra, linear equations, linear motion, and television programming.
When a user asks Claude or ChatGPT: "What are the best tools for sprint planning?", the model must determine whether "Linear" refers to the project management software founded in 2019 or a generic mathematical concept.
Whether additional public identifiers affect an AI answer is an empirical question. Test it with a documented prompt set and avoid assuming the system uses a particular knowledge graph.
The Solution: Linked Data Anchoring with sameAs & knowsAbout
Schema.org JSON-LD provides machine-readable descriptions of the content on a page. It should match visible, accurate page content and is not a guarantee that an AI product will use or cite that page.
Use sameAs only for verified profiles that refer to the same organization. Incorrect identifiers can confuse readers and automated systems.
Describe products and areas of work precisely. Do not add expertise, ratings, or customer claims that cannot be supported.
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Example Organization",
"url": "https://www.example.com",
"description": "A concise description that matches the visible content of this organization's website."
}How an Audit Works
Submit the same buyer prompt to the model routes configured for your account. Review the returned answers, brand mentions, sentiment labels, and source URLs.