Triple

T13808108
Position Surface form Disambiguated ID Type / Status
Subject Lesja municipality E331811 entity
Predicate formerCounty P1069 FINISHED
Object Oppland county E98763 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: Oppland county | Statement: [Lesja municipality, formerCounty, Oppland county]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oppland county
Context triple: [Lesja municipality, formerCounty, Oppland county]
  • A. Oppland chosen
    Oppland is a former inland county in southeastern Norway known for its mountainous terrain, national parks, and popular skiing and hiking areas.
  • B. Trøndelag County
    Trøndelag County is a large region in central Norway known for its historic city of Trondheim, coastal and fjord landscapes, and role as a cultural and economic hub of the country.
  • C. Nordland county
    Nordland county is a long, coastal region in northern Norway known for its dramatic fjords, islands, and Arctic landscapes.
  • D. Akershus county
    Akershus county was a former county in southeastern Norway that historically surrounded Oslo and included both urban suburbs and rural areas before being merged into Viken county.
  • E. Hedmark
    Hedmark is a former county in eastern Norway known for its vast forests, agriculture, and inland landscapes along the Swedish border.
  • 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_69d81c59f8808190a851bc56afdc55e9 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de026eae8481908b8880635e6a9152 completed April 14, 2026, 9:01 a.m.
NED1 Entity disambiguation (via context triple) batch_6a01953c493c819084850ab8e7f0d261 completed May 11, 2026, 8:37 a.m.
Created at: April 9, 2026, 10:12 p.m.