Triple

T3542710
Position Surface form Disambiguated ID Type / Status
Subject Ålesund E74923 entity
Predicate locatedIn P40 FINISHED
Object Møre og Romsdal county E114915 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: Møre og Romsdal county | Statement: [Ålesund, locatedIn, Møre og Romsdal county]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Møre og Romsdal county
Context triple: [Ålesund, locatedIn, Møre og Romsdal county]
  • A. Møre og Romsdal chosen
    Møre og Romsdal is a coastal county in western Norway known for its dramatic fjords, islands, and mountainous landscapes.
  • B. Sogn og Fjordane
    Sogn og Fjordane was a former county in western Norway known for its dramatic fjords, mountains, and coastal landscapes.
  • C. Oppland
    Oppland is a former inland county in southeastern Norway known for its mountainous terrain, national parks, and popular skiing and hiking areas.
  • D. Rogaland
    Rogaland is a county in southwestern Norway known for its rugged coastline, fjords, and the oil industry centered around the city of Stavanger.
  • E. Hordaland
    Hordaland was a former county in western Norway known for its fjords, coastal landscapes, and the city of Bergen.
  • 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_69ad85d274cc8190ab59c97298a1cfbf completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbf73c5e881909a8352512928377b completed March 8, 2026, 6:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4faf333d4819094293a326196fe50 completed March 14, 2026, 6:06 a.m.
Created at: March 8, 2026, 3:20 p.m.