Entity Extraction [FREE TOOL]
Elevate your website’s performance with our AI-powered entity linker and extraction tool.
See the entities AI can recognize and understand in your content.
Paste text or analyze a webpage. WordLift detects people, organizations, places, products, brands and other meaningful entities, then resolves them against Wikidata when a confident match is available.
Use it to see how your content can be transformed from text into structured, machine-readable knowledge — the foundation for knowledge graphs, semantic SEO and AI applications.
Try it below — no setup required.
Supports English, Italian, French, German, Spanish, Portuguese, Japanese and Chinese.
Build your entity layer for AI
Turn your content, products and business knowledge into a structured entity layer that search engines, AI assistants and agents can understand and use.
What happens after entity extraction?
Finding a name in text is only the first step.
Content Analysis v3 also tries to resolve that mention to a stable entity in Wikidata, so “Rome” becomes a machine-readable identity, not just a string.
When the evidence is strong enough, the system links the entity. When it is not, it can abstain instead of forcing a weak match.
That distinction matters when entities become inputs for knowledge graphs, structured data, retrieval systems and AI agents.Built for multilingual content
Content Analysis v3 supports entity detection and resolution across:
English · Italian · French · German · Spanish · Portuguese · Japanese · Chinese
Behind the demo is a knowledge base of more than 13.7 million Wikidata entities, combined with multilingual entity recognition, candidate retrieval and context-aware reranking.
Benchmarked, not just demonstrated
We benchmark Content Analysis v3 against Google Cloud Natural Language and public entity-linking datasets.
Our latest evaluation shows particularly strong performance across English, German and Spanish, while also highlighting where Google continues to perform better.
We publish the methodology, architecture and failure analysis because entity linking should be measurable — not a black box.
Read: Content Analysis v3 — From the Semantic Web to the Entity Layer of AI →
Why entities matter for AI
Search engines, AI assistants and agents increasingly operate on entities rather than isolated keywords.
A resolved entity gives a system something stable to reason about:
“Roma” → Rome → Q220 → Place → Italy → Lazio
That identity can then connect content to a knowledge graph, structured data, products, people, organizations, locations and other business concepts.
This is the foundation of the entity layer: a shared semantic layer between your content, your knowledge graph and the AI systems consuming them.
From a demo to your own knowledge graph
This tool shows the first part of the process.
WordLift uses the same principles to help organizations create and maintain their own entity layer: reconciling content, products and business concepts into a governed knowledge graph that can support search, AI visibility and agentic applications.
Want to understand how AI systems see your business?