Triple

T16509962
Position Surface form Disambiguated ID Type / Status
Subject Nordland E401032 entity
Predicate hasMunicipality P847 FINISHED
Object Brønnøy NE NERFINISHED

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: Brønnøy | Statement: [Nordland, hasMunicipality, Brønnøy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brønnøy
Context triple: [Nordland, hasMunicipality, Brønnøy]
  • A. Brønnøy chosen
    Brønnøy is a coastal municipality in Nordland county, Norway, known for its fishing communities and the distinctive Torghatten mountain with a natural hole through it.
  • B. Andøy
    Andøy is a municipality and island area in Nordland county, Norway, known for its Arctic landscapes, fishing communities, and whale-watching opportunities.
  • C. Nøtterøy
    Nøtterøy is a large, populated island and former municipality in Vestfold, Norway, situated in the Oslofjord and known for its coastal landscapes and residential communities.
  • D. Røst
    Røst is a small, remote island and fishing community in northern Norway, known for its dramatic coastal scenery, rich seabird colonies, and traditional cod fisheries.
  • E. Askøy
    Askøy is a large island and municipality on Norway’s west coast, situated near Bergen and known for its coastal landscapes and commuter links to the city.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d88381f6148190819958a038be990e completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e32e54f7508190804bbae4c9bc8fe3 completed April 18, 2026, 7:10 a.m.
Created at: April 10, 2026, 5:14 a.m.