How to Extract JSON-LD from Web Pages and Verify Its Claims
Inventory embedded JSON-LD with a tested Python example, select the right entity, and check extracted fields against readable page evidence.
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Inventory embedded JSON-LD with a tested Python example, select the right entity, and check extracted fields against readable page evidence.
Preserve table header paths, raw cell text, units, notes and sources before normalizing web data. Includes tested acquisition and parsing examples plus review rules.
Choose a single-page Fetch, query-driven Search, or bounded site crawl by the URLs you know and the coverage you need. Includes a tested public probe and verification checklist.
Check extracted documents for missing facts, sidebar noise, broken structure, and unknown provenance before admitting them to model context.
Extract useful links as reviewable records that retain origin page, raw and resolved URL, anchor text, nearby context, scope, and failure evidence.
Choose HTML, Markdown, or structured JSON from the next operation's contract, then preserve provenance and validate what each transformation keeps or drops.
Choose Search for source discovery and a scraping or page-read API for known-URL extraction, then connect them with a verifiable handoff contract.
Choose between page extraction and browser interaction using task contracts, verified outcomes, session boundaries, and a tested public-page read example.
Compare web search API response contracts, bundled content, source provenance, and verification gates before choosing an integration for your AI agent.