Domain-specific labels
Agree on the entities and distinctions that matter to your use case.
Entity recognition
Find the names and facts inside text
NakkebNER scopes entity extraction for documents or records, such as people, organizations, places, dates, and amounts. A sample from your domain is used to define labels, evaluate accuracy, and decide where human review belongs.
01 / Scope
What NakkebNER covers
Evidence in context
Return text spans and source references so extracted entities can be checked.
Reviewable quality
Evaluate precision and omissions on representative material before integration.
02 / Method
A clear path through the work.
- 01
Define the labels
Choose entity types and prepare examples that reflect your content.
- 02
Annotate and evaluate
Review a sample, test extraction, and inspect common errors.
- 03
Integrate carefully
Agree on output shape, thresholds, and human review points.
03 / Outputs
Know what your project includes.
- Entity label guide
- Annotated sample and evaluation
- Integration specification
Specific scope, compatibility, schedule, and commercial terms are confirmed in a project agreement.
04 / Questions
A few useful details.
Which languages are supported?
Language feasibility is evaluated against your sample and requirements; historical claims are not current guarantees.
Can it infer relationships?
Relationship extraction needs a separate definition and evaluation beyond identifying names.
Is it suitable for sensitive text?
Data handling and deployment choices must be agreed before any sample is processed.
Your next useful answer
Bring us your business question.
We’ll connect your information requirements to the right Nakkeb product, service, or implementation scope.