Triple

T13521496
Position Surface form Disambiguated ID Type / Status
Subject Kvæfjord E322907 entity
Predicate hasSettlement P1068 FINISHED
Object Borkenes E408942 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: Borkenes | Statement: [Kvæfjord, hasSettlement, Borkenes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Borkenes
Context triple: [Kvæfjord, hasSettlement, Borkenes]
  • A. Borkenes chosen
    Borkenes is a small coastal village in northern Norway known for its scenic surroundings and traditional fishing and farming activities.
  • B. Brønnøya
    Brønnøya is a scenic island in the Oslofjord known for its holiday homes, nature trails, and car-free environment, located within the municipality of Asker in Norway.
  • C. Rennesøy
    Rennesøy is an island and former municipality in Rogaland county, southwestern Norway, known for its coastal landscape and proximity to the city of Stavanger.
  • D. Øye
    Øye is a small Norwegian village in the Sunnmøre region, known for its dramatic fjord landscape and the historic Hotel Union Øye.
  • E. Kvitøya
    Kvitøya is a remote, mostly ice-covered island in the far northeastern part of the Svalbard archipelago in the Arctic Ocean.
  • 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_69d80766a21881909f21a1b7421d3b8a completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbafa3df0c8190804174695587f0ea completed April 12, 2026, 2:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff7559f0448190a992f0770ac8227a completed May 9, 2026, 5:56 p.m.
Created at: April 9, 2026, 9:44 p.m.