Four entity types
PER (person), ORG (organization), LOC (location) and MISC (everything else).
CatalogLatency Typically 0.5–2 seconds Async false Rate Limit Per API key Max Input A few thousand characters per text
cn8/text-ner
Send a `texts` array and get the named entities in each one. For every text you
Learn more about Named Entity Recognition Text NLPsyncInstant previewtextanalysisnlp
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Overview
Send a texts array and get the named entities in each one. For every text you receive a flat entities list (with the matched text, its type, and a confidence score) plus four ready-to-use lists grouped by type: persons, organizations, locations and misc. Responses are synchronous — one request, one set of results. Ideal for indexing documents, enriching content and auto-filling forms.
Key capabilities
Grouped lists
Ready-made persons, organizations, locations and misc arrays, alongside the flat entities list.
Confidence per entity
Every entity comes with a score from 0 to 1.
Multilingual
Recognizes entities across many languages out of the box.
When to use it
Document indexing
Pull out key entities for search facets and knowledge graphs.
Form auto-fill
Pre-fill name, company and location fields from unstructured text.
Content enrichment
Tag articles with the people, organizations and places they mention.
Input & output
input
A texts array of strings — one set of results is produced per text.
JSON body
output
results[] with an entities list plus grouped persons / organizations / locations / misc arrays.
JSON
Guides & tips
How it works
- Each text is scanned for named entities, which are returned in a flat
entitieslist with atypeand a confidencescore. - The same entities are also grouped for you into
persons,organizations,locationsandmisclists, deduplicated as plain strings. - One request can include several texts in the
textsarray; you get one results[] entry per text, and the price is per request.
Entity types
- PER — people (e.g. Steve Jobs, Angela Merkel)
- ORG — organizations (e.g. Apple Inc, United Nations)
- LOC — locations (e.g. Cupertino, France)
- MISC — everything else recognized (events, products, nationalities, …)
Specs
Schema
Request body
textsrequiredarray<string>Array of texts to analyze. One results[] entry is returned per text.
Response
results[].entitiesarray<object>Flat entity list, each {text, type, score}. type is one of PER, ORG, LOC, MISC.
results[].personsarray<string>PER entities as a deduplicated list of strings.
results[].organizationsarray<string>ORG entities as a deduplicated list of strings.
results[].locationsarray<string>LOC entities as a deduplicated list of strings.
results[].miscarray<string>MISC entities as a deduplicated list of strings.
Pricing
Billed per request.
| Service | Unit | Price |
|---|---|---|
| Named Entity Recognition | item | $0.004/request |