Triple

T22574858
Position Surface form Disambiguated ID Type / Status
Subject Frisk Asker E544365 entity
Predicate formerName P65 FINISHED
Object IF Frisk Asker 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: IF Frisk Asker | Statement: [Frisk Asker, formerName, IF Frisk Asker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: IF Frisk Asker
Context triple: [Frisk Asker, formerName, IF Frisk Asker]
  • A. Frisk Asker chosen
    Frisk Asker is a Norwegian sports club best known for its ice hockey team, which competes at the top level of Norwegian hockey.
  • B. Asker Fotball
    Asker Fotball is a Norwegian football club based in Asker, known for competing in the national league system and developing local talent.
  • C. Vålerenga
    Vålerenga is a neighborhood in Oslo, Norway, known for its working-class roots and strong association with the local football club Vålerenga Fotball.
  • D. Storhamar Idrettslag
    Storhamar Idrettslag is a Norwegian multi-sport club from Hamar best known for its successful ice hockey section, Storhamar Hockey.
  • E. Mjøndalen
    Mjøndalen is a town in Viken county, Norway, known historically for its industry and for its football club Mjøndalen IF.
  • 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_69e11e30d05481909df915354c89f0d6 completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f15fec296481908a6101b02e5bedaa completed April 29, 2026, 1:33 a.m.
Created at: April 16, 2026, 8:53 p.m.