Triple

T22611629
Position Surface form Disambiguated ID Type / Status
Subject Lørenskog municipality E566718 entity
Predicate hasSportsClub P346 FINISHED
Object Lørenskog IK 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: Lørenskog IK | Statement: [Lørenskog municipality, hasSportsClub, Lørenskog IK]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lørenskog IK
Context triple: [Lørenskog municipality, hasSportsClub, Lørenskog IK]
  • A. Lørenskog IF chosen
    Lørenskog IF is a Norwegian sports club best known for its football team, based in Lørenskog near Oslo.
  • B. Hasle-Løren IL
    Hasle-Løren IL is a Norwegian sports club from Oslo known for its ice hockey program that helped develop NHL player Mats Zuccarello.
  • C. Lillehammer IK
    Lillehammer IK is a Norwegian ice hockey club based in Lillehammer that competes in the country’s top leagues.
  • D. Storhamar Idrettslag
    Storhamar Idrettslag is a Norwegian multi-sport club from Hamar best known for its successful ice hockey section, Storhamar Hockey.
  • E. Kongsvinger IL
    Kongsvinger IL is a Norwegian sports club best known for its football team, which has competed in the country’s top divisions.
  • 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_69e245884860819081046ce07d5872c4 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f167eb08c88190bf2380fa8575d2da completed April 29, 2026, 2:07 a.m.
Created at: April 17, 2026, 2:56 p.m.