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An entity is a real-world thing a sentence names: a person, a place, a date, an amount. NER teaches machines to spot them. 🔎

Most of the value in text hides in its names. Who, where, when, and how much answer the questions people actually care about.

The most common entity is the PERSON: a named human like Marie Curie or Lionel Messi. NER pulls these out even mid-sentence.

Locations such as Paris, the Nile, or Japan are tagged as GPE or LOC. Geography is one of the richest sources of entities.

Companies, teams, and agencies are ORG entities. Tesla, NASA, and Real Madrid all belong to this group.

Phrases like Monday or next summer become DATE entities. They let you anchor events on a timeline automatically.

Amounts like $5 million or 200 are tagged MONEY or QUANTITY. These power finance and analytics pipelines.

Each entity carries a label that names its category. The label is what makes the extracted text actually useful downstream.

Common words like happy or running are not entities. NER only flags named things, not ordinary vocabulary or adjectives.

Apple can be a fruit or a company. NER uses surrounding context to choose the right label instead of guessing blindly.

From scanning resumes to flagging news, NER turns messy paragraphs into a clean list of who, where, and when. 🚀

Which word would NER most likely tag as an entity?

Entities are the named things in text: people, places, orgs, dates, money. Each gets a label, and context picks the right one. ✅