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

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A named entity is a "real-world object" that's assigned a name for example, a
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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`:
```python
### {executable="true"}
import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp("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`.
Documentation for Entity Linking (#4065) * document token ent_kb_id * document span kb_id * update pipeline documentation * prior and context weights as bool's instead * entitylinker api documentation * drop for both models * finish entitylinker documentation * small fixes * documentation for KB * candidate documentation * links to api pages in code * small fix * frequency examples as counts for consistency * consistent documentation about tensors returned by predict * add entity linking to usage 101 * add entity linking infobox and KB section to 101 * entity-linking in linguistic features * small typo corrections * training example and docs for entity_linker * predefined nlp and kb * revert back to similarity encodings for simplicity (for now) * set prior probabilities to 0 when excluded * code clean up * bugfix: deleting kb ID from tokens when entities were removed * refactor train el example to use either model or vocab * pretrain_kb example for example kb generation * add to training docs for KB + EL example scripts * small fixes * error numbering * ensure the language of vocab and nlp stay consistent across serialization * equality with = * avoid conflict in errors file * add error 151 * final adjustements to the train scripts - consistency * update of goldparse documentation * small corrections * push commit * typo fix * add candidate API to kb documentation * update API sidebar with EntityLinker and KnowledgeBase * remove EL from 101 docs * remove entity linker from 101 pipelines / rephrase * custom el model instead of existing model * set version to 2.2 for EL functionality * update documentation for 2 CLI scripts
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> - **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](/usage/visualizers), here's what
our example sentence and its named entities look like:
import DisplaCyEntHtml from 'images/displacy-ent1.html'; import { Iframe } from
'components/embed'
<Iframe title="displaCy visualization of entities" html={DisplaCyEntHtml} height={100} />