spaCy/website/docs/usage/101/_named-entities.md

A named entity is a "real-world object" that's assigned a name – for example, a person, a country, a product or a book title. spaCy can recognize various types of named entities in a document, by asking the model for a prediction. Because models are statistical and strongly depend on the examples they were trained on, this doesn't always work perfectly and might need some tuning later, depending on your use case.

Named entities are available as the ents property of a Doc:

### {executable="true"}
import spacy

nlp = spacy.load("en_core_web_sm")
doc = nlp(u"Apple is looking at buying U.K. startup for $1 billion")

for ent in doc.ents:
    print(ent.text, ent.start_char, ent.end_char, ent.label_)

• Text: The original entity text.
• Start: Index of start of entity in the Doc.
• End: Index of end of entity in the Doc.
• LabeL: Entity label, i.e. type.

Text	Start	End	Label	Description
Apple	0	5	ORG	Companies, agencies, institutions.
U.K.	27	31	GPE	Geopolitical entity, i.e. countries, cities, states.
$1 billion	44	54	MONEY	Monetary values, including unit.

Using spaCy's built-in displaCy visualizer, here's what our example sentence and its named entities look like:

import DisplaCyEntHtml from 'images/displacy-ent1.html'; import { Iframe } from 'components/embed'