Triple

T18113290
Position Surface form Disambiguated ID Type / Status
Subject Vang E433534 entity
Predicate containsSettlement P847 FINISHED
Object Øye 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: Øye | Statement: [Vang, containsSettlement, Øye]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Øye
Context triple: [Vang, containsSettlement, Øye]
  • A. Øye chosen
    Øye is a small Norwegian village in the Sunnmøre region, known for its dramatic fjord landscape and the historic Hotel Union Øye.
  • B. Andøya
    Andøya is a large Norwegian island in Nordland county, known for its dramatic coastal landscapes, space center, and rich bird and whale-watching opportunities.
  • C. Sørøya
    Sørøya is a large, sparsely populated island in northern Norway known for its rugged coastal landscapes, rich fishing grounds, and opportunities for outdoor activities such as hiking and sea angling.
  • D. Askøy island
    Askøy island is a large Norwegian island in Vestland county, known for its coastal landscapes and proximity to the city of Bergen.
  • E. Ryøya
    Ryøya is an island located in northern Norway near the municipality of Balsfjord, known for its Arctic coastal landscape.
  • 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_69d8b90916008190a1f110bd7ced5473 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4ddd3fd9c81909bfe95927f7553e3 completed April 19, 2026, 1:51 p.m.
Created at: April 10, 2026, 10:28 a.m.