Triple

T5098073
Position Surface form Disambiguated ID Type / Status
Subject Møre og Romsdal E114915 entity
Predicate containsSettlement P847 FINISHED
Object Kristiansund E76076 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: Kristiansund | Statement: [Møre og Romsdal, containsSettlement, Kristiansund]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kristiansund
Context triple: [Møre og Romsdal, containsSettlement, Kristiansund]
  • A. Kristiansund chosen
    Kristiansund is a coastal city in western Norway known for its historic clipfish industry, distinctive layout across several islands, and picturesque harbor.
  • B. Kristiansand
    Kristiansand is a coastal city in southern Norway known for its harbor, beaches, and role as a regional cultural and economic center.
  • C. 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.
  • D. Ålesund
    Ålesund is a coastal Norwegian city renowned for its distinctive Art Nouveau architecture and location across several islands in Western Norway.
  • E. Sogndal
    Sogndal is a village and municipality in Vestland county, Norway, known for its scenic fjord landscape, agriculture, and as a regional education and service center.
  • 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_69bd443fc49c819089629c00e311310c completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd7567d21081909227ed8f08b74c71 completed March 20, 2026, 4:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf9f5291f881909d9470a728667346 completed March 22, 2026, 7:50 a.m.
Created at: March 20, 2026, 1:40 p.m.