Triple

T13953927
Position Surface form Disambiguated ID Type / Status
Subject Jevnaker municipality E335604 entity
Predicate countrySubdivision P766 FINISHED
Object Viken county E50816 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: Viken county | Statement: [Jevnaker municipality, countrySubdivision, Viken county]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Viken county
Context triple: [Jevnaker municipality, countrySubdivision, Viken county]
  • A. Viken county chosen
    Viken county is an administrative region in southeastern Norway that includes several municipalities and borders Sweden and the Oslofjord.
  • B. Skåne County
    Skåne County is Sweden’s southernmost county, known for its fertile farmland, coastal landscapes, and major cities such as Malmö and Lund.
  • C. Östergötland County
    Östergötland County is an administrative region in southeastern Sweden known for its mix of historic cities, fertile plains, and coastal and archipelago landscapes along the Baltic Sea.
  • D. Västmanland County
    Västmanland County is an administrative region in central Sweden known for its mix of industrial towns, forests, and lakes.
  • E. Halland County
    Halland County is a coastal county in southwestern Sweden known for its beaches along the Kattegat, agriculture, and proximity to the city of Gothenburg.
  • 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_69d81c6081b88190b53e317c3370c8fe completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2e146720819085d0f5eae558b7a4 completed April 14, 2026, 12:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcdef3f31c8190a2fc3eed316756d6 completed May 7, 2026, 6:50 p.m.
Created at: April 9, 2026, 10:17 p.m.