Triple

T5291225
Position Surface form Disambiguated ID Type / Status
Subject Klanen E119744 entity
Predicate collaboratesWith P37 FINISHED
Object Vålerenga Fotball management E22610 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 Fotball management | Statement: [Klanen, collaboratesWith, Vålerenga Fotball management]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vålerenga Fotball management
Context triple: [Klanen, collaboratesWith, Vålerenga Fotball management]
  • A. Vålerenga
    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 chosen
    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. Skeid Fotball
    Skeid Fotball is a Norwegian football club from Oslo known for its historic presence in the national leagues and local rivalries with other Oslo teams.
  • D. Viking FK
    Viking FK is a Norwegian professional football club based in Stavanger that competes in the country’s top division, the Eliteserien.
  • E. Bryne FK
    Bryne FK is a Norwegian football club known for developing striker Erling Haaland in its youth system.
  • 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_69bf06f066988190a3df7e270df84fdd completed March 21, 2026, 9 p.m.
Created at: March 20, 2026, 1:52 p.m.