Triple

T14601474
Position Surface form Disambiguated ID Type / Status
Subject Stange E342714 entity
Predicate neighboringMunicipality P17964 FINISHED
Object Åsnes E482368 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: Åsnes | Statement: [Stange, neighboringMunicipality, Åsnes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Åsnes
Context triple: [Stange, neighboringMunicipality, Åsnes]
  • A. Åsnes chosen
    Åsnes is a rural municipality in Innlandet county in eastern Norway, known for its forests, agriculture, and location in the traditional region of Solør.
  • B. Lysnes
    Lysnes is a small coastal village in northern Norway, situated within the former Lenvik municipality in Troms county.
  • C. Tvedestrand
    Tvedestrand is a coastal town and municipality in southern Norway known for its wooden houses, maritime heritage, and picturesque archipelago.
  • D. Hemnes
    Hemnes is a municipality in Nordland county, Norway, known for its mountainous landscapes, fjords, and outdoor recreation opportunities.
  • E. Kragerø
    Kragerø is a coastal town in Norway renowned for its picturesque archipelago, historic wooden buildings, and role as a popular summer holiday destination.
  • 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_69d822dec68081908c2553145c4051dc completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb438748081908020ce04b869866a completed April 14, 2026, 9:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69feadfaddc88190bb1196ace0bfd4ff completed May 9, 2026, 3:46 a.m.
Created at: April 10, 2026, 1:25 a.m.