Triple

T20367298
Position Surface form Disambiguated ID Type / Status
Subject Kvenndalsfjellet wind farm E496946 entity
Predicate locatedIn P40 FINISHED
Object Trøndelag 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: Trøndelag | Statement: [Kvenndalsfjellet wind farm, locatedIn, Trøndelag]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Trøndelag
Context triple: [Kvenndalsfjellet wind farm, locatedIn, Trøndelag]
  • A. Trøndelag chosen
    Trøndelag is a central region of Norway known for its historic city of Trondheim, coastal landscapes, and strong cultural traditions.
  • B. Buskerud
    Buskerud is a former county in southeastern Norway known for its varied landscape of forests, rivers, and mountains, including parts of the Hallingdal valley and Hardangervidda plateau.
  • C. Møre og Romsdal
    Møre og Romsdal is a coastal county in western Norway known for its dramatic fjords, islands, and mountainous landscapes.
  • D. Nord-Valdres
    Nord-Valdres is the northern part of the traditional Valdres district in Innlandet county, Norway, known for its mountainous landscapes, valleys, and rural communities.
  • E. Hedmarken
    Hedmarken is a traditional district in Innlandet county in eastern Norway, known for its agricultural landscapes and its central town, Hamar.
  • 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_69e0b4a4f9b081908a5a021919c21ccb completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6787291d88190a526fe2461d2a7c6 completed April 20, 2026, 7:03 p.m.
Created at: April 16, 2026, 11:26 a.m.