Tag your entity types 61% better than AWS Comprehend, 99% cheaper

SIE is the open-source inference engine serving NuNER Zero-span. Give the model your own entity types and get each match at its exact position, without labelling examples or training a model.

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SIE · NuNER Zero-span

Every entity found

He studied physics at the University of Heidelberg , the University of Lausanne , and , ultimately , the Humboldt University of Berlin under Max Planck .

CrossNER science (MIT licence) ↗

Fixed-type detectors answer in their own types, not yours

He studied physics at the University of Heidelberg , the University of Lausanne , and , ultimately , the Humboldt University of Berlin under Max Planck .

Asked for: discipline, university, scientist and 14 more types

CrossNER science (MIT licence) ↗ Recorded replies ↗

AWS Comprehend

Answers in ORGANIZATION and PERSON

ORGANIZATION: University of Heidelberg; University of Lausanne; Humboldt University of Berlin; Planck
PERSON: Max

spaCy en_core_web_trf

Answers in ORG and PERSON

ORG: the University of Heidelberg; the University of Lausanne; the Humboldt University of Berlin
PERSON: Max Planck

Keep the spans each reply typed university

AWS Comprehend finds 0 of 3spaCy finds 0 of 3

docs.aws.amazon.com/comprehend/latest/dg/cer-annotation.html AWS Comprehend Custom types require labelled training examples
Annotations

“For plain-text annotations, provide at least three annotated input documents and at least 25 annotations per entity.”
Read on 14 October 2026

61% better at entity tagging than AWS Comprehend, 99% cheaper

Run this eval

Exact spans and types (F1)

652 sentences ↑

Claude Sonnet 5.5

Claude Sonnet 5.5 79.6%

GPT-6.1 Sol

GPT-6.1 Sol 79.2%

Claude Haiku 5.5

Claude Haiku 5.5 73.7%

GPT-6 Luna

GPT-6 Luna 69.4%

SIE NuNER Zero-span

SIE NuNER Zero-span 63.0%

AWS Comprehend

39.0% AWS Comprehend

Cost per 1M sentences

USD ↓

SIE NuNER Zero-span

$1.38 SIE NuNER Zero-span

GPT-6 Luna

$159 GPT-6 Luna

Claude Haiku 5.5

$183 Claude Haiku 5.5

AWS Comprehend

$307 AWS Comprehend

Claude Sonnet 5.5

Claude Sonnet 5.5 $1,804

GPT-6.1 Sol

GPT-6.1 Sol $2,160

Evaluation

Tested: 652 sentences across seven public entity datasets
Scored: Exact span and type micro-F1; best recorded LLM settings; mapped incumbent types
Prices: Serverless and list prices; AWS first tier, 300-character minimum

Get typed spans and exact offsets

How we tested

from sie_sdk import SIEClient
client = SIEClient(api_key="API keysk-sie-…")
result = client.extract(
    "modelnumind/NuNER_Zero-span",
    {"text": "textHe studied physics at the University of Heidelberg , the University of Lausanne , and , ultimately , the Humboldt University of Berlin under Max Planck ."},
    labels=[
        "labelmiscellaneous",
        "labelorganisation",
        "labelperson", "labelcountry",
        "labellocation", "labelevent",
        "labelaward", "labeluniversity",
        "labelacademic journal",
        "labelastronomical object",
        "labelchemical compound",
        "labelchemical element",
        "labeldiscipline", "labelenzyme",
        "labelprotein", "labelscientist",
        "labeltheory",
    ],
    options={"threshold":0.5},
)
for span in result["entities"]:
    print(span["label"], span["text"])

import { SIEClient } from '@superlinked/sie-sdk';

const client = new SIEClient(undefined, { apiKey: 'sk-sie-…' });

const result = await client.extract(
  'numind/NuNER_Zero-span',
  { text: "He studied physics at the University of Heidelberg , the University of Lausanne , and , ultimately , the Humboldt University of Berlin under Max Planck ." },
  { labels: ["miscellaneous","organisation","person","country","location","event","award","university","academic journal","astronomical object","chemical compound","chemical element","discipline","enzyme","protein","scientist","theory"], adapterOptions: {"threshold":0.5} },
);
console.log(result.entities);

curl https://api.superlinked.com/v1/extract/numind%2FNuNER_Zero-span \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"items\":[{\"text\":\"He studied physics at the University of Heidelberg , the University of Lausanne , and , ultimately , the Humboldt University of Berlin under Max Planck .\"}],\"params\":{\"labels\":[\"miscellaneous\",\"organisation\",\"person\",\"country\",\"location\",\"event\",\"award\",\"university\",\"academic journal\",\"astronomical object\",\"chemical compound\",\"chemical element\",\"discipline\",\"enzyme\",\"protein\",\"scientist\",\"theory\"],\"options\":{\"threshold\":0.5}}}"

Build the "Named entities" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: numind/NuNER_Zero-span (SIE primitive: /extract). Keep the model id configurable.

Task
- Input: a block of text.
- Behaviour: return the named entities found in the text with their types
- Call the selected SIE primitive once per request and map the response into your domain type.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.

Run

Output

Spanstart–end

Discipline: physics11–18
University: University of Heidelberg26–50; University of Lausanne57–79; Humboldt University of Berlin105–134
Scientist: Max Planck141–151

Tag your own entity types wherever your text lives

Serverless API

We host the models; you call one API.

No idle GPUs, pay for what you use
Fits your stack: SDK, API, CLI, MCP
Self-host the same open-source engine
SOC 2 Type 2, US or EU data residency
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Get started

Self-hosted (Kubernetes)

Your data stays in your cloud, backed by our engineers.

Terraform modules for your cloud
Apache-2.0, same engine as Serverless
GPU workers scale to zero between jobs
Separate worker pools for each tenant

Agent prompt Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Google Cloud project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Azure subscription with the superlinked/sie/azure Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Alibaba Cloud account with the superlinked/sie/alicloud Terraform module. Docs: superlinked.com/docs/deployment.

Talk to us Deploy guide

Run locally

The open-source server on your own machine.

Runs on your NVIDIA GPU
One container, no cluster
Process your data fully offline
Same SDK and model IDs as Serverless

docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-default

Quickstart

Switch to SIE in 5 minutes

import boto3

comprehend = boto3.client("comprehend")
response = comprehend.detect_entities(
    Text=text, LanguageCode="en",
)
# Comprehend's nine built-in types; your own need a trained custom recognizer
entities = response["Entities"]

import json
from openai import OpenAI

client = OpenAI(api_key="sk-…")
response = client.responses.create(
    model="gpt-6-luna", input=messages,
    text={"format": {"type": "json_schema", "name": "entities",
                     "strict": True, "schema": schema}},
)
entities = json.loads(response.output_text)["entities"]

SIE

from sie_sdk import SIEClient

client = SIEClient(api_key="sk-sie-…")
entities = client.extract(
    "numind/NuNER_Zero-span",
    {"text": text},
    labels=["discipline", "university", "scientist"],  # your types
)["entities"]

from sie_sdk import SIEClient

client = SIEClient(api_key="sk-sie-…")
entities = client.extract(
    "numind/NuNER_Zero-span",
    {"text": text},
    labels=["discipline", "university", "scientist"],  # your types
)["entities"]