Named Entity Recognition (batch)

Extract entities from up to 64 multilingual texts in one request.

POST/api/models/ner/batch

Authorizations

Bearer

Authorizationstringrequiredheader

Required Bearer API key. Programmatic access to the World API needs a key; browsing nosible.world in a browser does not.

Example: Bearer nos_sk_...

Body

application/json

textsstring[]requiredbody

Between 1 and 64 texts, each at most 8,000 characters.

Example: ["NVIDIA opened a new office in Johannesburg.","SAP expanded in Berlin."]

How to use this endpoint

guidance

Use Named Entity Recognition (batch) when you have multiple texts to classify. Each item in results uses the same text and predictions shape as the single-text NER response, in the same order as the request.

Response body

application/json

These are the fields you can build against. Nested names use dot notation; optional sections are called out in their descriptions.

schemastring

Stable classifier response schema shared by the NER endpoints.

Example: nosible_model_classification_v1

modelstring

Pinned classifier name, version, and immutable build identifier.

Example: NOSIBLE/gliner-ner-v1@revision:d306c21d9c56b6688a5353e091ddb04d37b2b3f9

resultsobject[]

One NER result per input text, in input order.

Example: [{ "text": "NVIDIA opened...", "predictions": [{ "label": "ORG", "score": 0.99, "text": "NVIDIA", "start": 0, "end": 6 }] }]

results[].textstring

The exact input text classified for this result.

Example: NVIDIA opened a new office in Johannesburg.

results[].predictionsobject[]

Entities detected in this text, using PERSON, ORG, GPE, LOC, or PRODUCT labels.

Example: [{ "label": "ORG", "score": 0.99, "text": "NVIDIA", "start": 0, "end": 6 }]

results[].predictions[].labelenum

One of PERSON, ORG, GPE, LOC, or PRODUCT.

Example: ORG

results[].predictions[].scorenumber

Model confidence score.

Example: 0.99

results[].predictions[].textstring

The entity surface text.

Example: NVIDIA

results[].predictions[].start/endinteger

Half-open character offsets into the exact input text.

Example: 0 / 6

Responses and errors

HTTP

200Request succeeded. The response body is shown in the panel on the right.

400invalid_request — The request is syntactically valid JSON but a value, date, filter, or combination of fields is invalid.

401unauthorized — The required API key or Bearer credential is missing or invalid.

422validation_error — The request shape is understood but one or more values fail validation.

429rate_limited — The request exceeded the account or public-window rate limit. Respect Retry-After when supplied.

502upstream_error — An upstream retrieval service failed while processing the request.

504upstream_timeout — An upstream retrieval service did not respond within the request timeout.