facebookresearch/BLINK

Health v2 · maintenance only

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1210 stars · 233 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

BLINK is a Python entity linking library from Facebook Research that resolves mentions in text to Wikipedia entities using a two-stage bi-encoder/cross-encoder BERT architecture. It includes FAISS-based dense retrieval and ELQ for end-to-end entity linking on questions.

Use cases

• link entity mentions in text to wikipedia pages
• wikification of documents
• build a knowledge base entity linker
• dense retrieval of entities with faiss
• end-to-end entity linking for questions
• disambiguate mentions against a knowledge base

When to choose

• you need state-of-the-art entity linking against Wikipedia
• you want dense bi-encoder retrieval with FAISS indexing
• you need end-to-end entity linking on questions via ELQ

When to avoid

• you need entity linking against a custom non-Wikipedia knowledge base without adaptation
• you need a maintained library with recent updates or modern Python support
• you want a lightweight solution without downloading large pretrained models

Facets

library · maturity maintenance

nlp machine-learning search-engine rag artificial-intelligence large-language-models python entity-linking wikification wikipedia bert faiss bi-encoder cross-encoder facebook-research natural-language-processing search linux gpu

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For agents

markdown · JSON · MCP: product_card(name="facebookresearch/BLINK")