Triple

T23483798
Position Surface form Disambiguated ID Type / Status
Subject Asker municipality E570473 entity
Predicate hasSportsClub P346 FINISHED
Object Asker Fotball 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: Asker Fotball | Statement: [Asker municipality, hasSportsClub, Asker Fotball]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Asker Fotball
Context triple: [Asker municipality, hasSportsClub, Asker Fotball]
  • A. Asker Fotball chosen
    Asker Fotball is a Norwegian football club based in Asker, known for competing in the national league system and developing local talent.
  • B. Frisk Asker
    Frisk Asker is a Norwegian sports club best known for its ice hockey team, which competes at the top level of Norwegian hockey.
  • C. Sandefjord Fotball
    Sandefjord Fotball is a Norwegian professional football club based in the town of Sandefjord, known for competing in the country’s top divisions.
  • D. Vålerenga
    Vålerenga is a neighborhood in Oslo, Norway, known for its working-class roots and strong association with the local football club Vålerenga Fotball.
  • E. Elverum Fotball
    Elverum Fotball is a Norwegian football club based in the town of Elverum, known for competing in the lower tiers of the national league system.
  • 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_69e245b0b01481908f636939bedd804c completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1a751e6a08190a42c36722275d5d3 completed April 29, 2026, 6:38 a.m.
Created at: April 17, 2026, 6:03 p.m.