Phase 4: NLP Applications · ~60 minutes · Python

Entity Linking & Disambiguation

NER found "Paris." Entity linking decides: Paris, France? Paris Hilton? Paris, Texas? Paris (the Trojan prince)? Without linking, your knowledge graph stays ambiguous.

What you will learn

Implement entity linking & disambiguation from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Build
Languages: Python
Prerequisites: Named Entity Recognition, Coreference Resolution
Time: ~60 minutes

The Problem

A sentence reads: "Jordan beat the press." Your NER tags "Jordan" as PERSON. Good. But which Jordan?

Michael Jordan (basketball)?
Michael B. Jordan (actor)?
Michael I. Jordan (Berkeley ML professor — yes, this confusion is real in ML papers)?
Jordan (the country)?
Jordan (Hebrew first name)?

Entity linking (EL) resolves each mention to a unique entry in a knowledge base: Wikidata, Wikipedia, DBpedia, or your domain KB. Two subtasks:

Candidate generation. Given "Jordan," which KB entries are plausible?
Disambiguation. Given the context, which candidate is the right one?

Both steps are learnable. Both are benchmarked. The combined pipeline has been stable for a decade — what changes is the quality of the disambiguator.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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