A JSON-based format for linked structured data, commonly embedded in a script so a page can describe entities and relationships without changing visible HTML.
A JSON-based format for expressing linked structured data, commonly embedded in a script element so a page can describe entities and relationships without changing visible HTML.
The decision and its boundary
JSON-LD is a way to serialize linked data in JSON. On web pages it is often placed in a script element and used with Schema.org vocabulary to describe the same entities a reader can see: an article, product, organization, breadcrumb, or event. It is a representation format, not a promise of a rich result. The graph must be syntactically valid and must agree with visible, current page facts.
What to inspect
Collect the rendered HTML rather than assuming a source template survived deployment. Parse the JSON-LD, identify each node and its identifiers, and compare important properties with the visible page. Check that URLs resolve, references point to intended entities, and values such as dates, prices, availability, author, and image are not stale. Validate warnings in context; a validator message is evidence to inspect, not automatically a defect.
A practical implementation path
Choose the smallest appropriate Schema.org type, model the primary entity first, and add linked entities only where the page exposes their facts. Generate values from the same structured source used by the visible template where possible. Validate a representative rendered page after release, then add regression checks for templates that produce many pages. Keep sitemap, canonical, and robots checks separate from JSON-LD validation.
Mistakes and measurement limits
The failure mode is schema theater: adding vocabulary that the page does not support in hopes of forcing a search feature. Hidden, invented, or contradictory properties create maintenance risk and can make the graph less trustworthy. I validate structured data against the visible page first, then inspect the graph. I fix real vocabulary or reference errors, not merely whatever makes a checker quiet.
Worked implementation example. For an editorial article, begin with the visible record: headline, byline, publication date, lead image, canonical URL, and publisher identity. Model that article in JSON-LD from the same content fields that render the page. Give the article one stable identifier and link the publisher and image only when those entities are real and resolvable. If the author, date, or image is absent from the visible page, do not invent it in the graph to satisfy a validator.
Check criteria. Fetch the rendered HTML after deployment, extract every JSON-LD script, and parse each graph. Compare headline, date, image, URL, and author values with the human-visible page. Confirm that identifiers and URLs resolve to the intended resources and that an article does not accidentally describe a different template entity. Run the relevant structured-data test, but classify findings: a parsing error, a vocabulary mismatch, a missing optional recommendation, and a search-feature eligibility note are different things.
Common errors. Do not paste a generic schema block into every page or mark a service, category, and product as the same primary entity. Do not use JSON-LD to hide prices, ratings, stock, reviews, or authors that users cannot see. A rich-result test cannot verify canonical, robots, sitemap, or content truthfulness, so those belong in the release check too. JSON-LD helps describe a page; it cannot compensate for an inaccessible or unhelpful page.