Triple

T13262595
Position Surface form Disambiguated ID Type / Status
Subject Finnsnes E315835 entity
Predicate countrySubdivision P766 FINISHED
Object Troms og Finnmark E81316 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Troms og Finnmark | Statement: [Finnsnes, countrySubdivision, Troms og Finnmark]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Troms og Finnmark
Context triple: [Finnsnes, countrySubdivision, Troms og Finnmark]
  • A. Troms og Finnmark chosen
    Troms og Finnmark is Norway’s northernmost and largest county, known for its Arctic landscapes, Sami culture, and phenomena like the midnight sun and northern lights.
  • B. Finnmark
    Finnmark is a sparsely populated, historically Norwegian region in the far northeast of Scandinavia, known for its Arctic climate, Sami culture, and dramatic coastal and tundra landscapes.
  • C. Hålogaland
    Hålogaland is a historical region in northern Norway traditionally encompassing parts of what are now Troms and Nordland counties.
  • D. Nordland county
    Nordland county is a long, coastal region in northern Norway known for its dramatic fjords, islands, and Arctic landscapes.
  • E. Møre og Romsdal
    Møre og Romsdal is a coastal county in western Norway known for its dramatic fjords, islands, and mountainous landscapes.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d806b1d9ac8190852c5571d5bd5f0f completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d9901b380881909e6520fbb6811084 completed April 11, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69f78acfd0e88190954533a7f282d83a completed May 3, 2026, 5:50 p.m.
Created at: April 9, 2026, 9:25 p.m.