Triple

T5943670
Position Surface form Disambiguated ID Type / Status
Subject Widerøe E132228 entity
Predicate headquartersLocation P62 FINISHED
Object Bodø E77659 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: Bodø | Statement: [Widerøe, headquartersLocation, Bodø]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bodø
Context triple: [Widerøe, headquartersLocation, Bodø]
  • A. Bodø chosen
    Bodø is a coastal city in northern Norway known as a regional hub for culture, transport, and access to Arctic nature.
  • B. Tromsø
    Tromsø is a city in northern Norway known for its Arctic location, vibrant cultural scene, and prominence as a viewing spot for the Northern Lights.
  • C. Porsgrunn
    Porsgrunn is an industrial and port city in Telemark county in southeastern Norway, known for its porcelain production and location along the Telemark Canal.
  • D. Kristiansund
    Kristiansund is a coastal city in western Norway known for its historic clipfish industry, distinctive layout across several islands, and picturesque harbor.
  • E. Florø
    Florø is a coastal town in western Norway known as the country’s westernmost town and a traditional center for fishing and maritime industries.
  • 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_69c00869d3308190af89b2453e0f7546 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c03937b4a88190819a1fd63fc3d3ed completed March 22, 2026, 6:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69c70adafa888190be606aba83a50302 completed March 27, 2026, 10:55 p.m.
Created at: March 22, 2026, 4:01 p.m.