Triple

T5291164
Position Surface form Disambiguated ID Type / Status
Subject Vålerenga Fotball E119743 entity
Predicate shortName P43 FINISHED
Object Vålerenga E124539 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: Vålerenga | Statement: [Vålerenga Fotball, shortName, Vålerenga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vålerenga
Context triple: [Vålerenga Fotball, shortName, Vålerenga]
  • A. Vålerenga chosen
    Vålerenga is a neighborhood in Oslo, Norway, known for its working-class roots and strong association with the local football club Vålerenga Fotball.
  • B. Vålerenga Fotball
    Vålerenga Fotball is a Norwegian professional football club based in Oslo, known for its passionate fan base and history in the country’s top division.
  • C. Mjøndalen
    Mjøndalen is a town in Viken county, Norway, known historically for its industry and for its football club Mjøndalen IF.
  • D. Bryne FK
    Bryne FK is a Norwegian football club known for developing striker Erling Haaland in its youth system.
  • E. Tromsø IL
    Tromsø IL is a Norwegian professional football club based in the city of Tromsø, known for competing in the country’s top divisions and for being one of the world’s northernmost elite clubs.
  • 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_69bd446de5648190b313a90bd96730d2 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd84eccac481908ba3fe28c3908d1d completed March 20, 2026, 5:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf8335f5a48190973622011df4c108 completed March 22, 2026, 5:50 a.m.
Created at: March 20, 2026, 1:52 p.m.