Triple

T12697237
Position Surface form Disambiguated ID Type / Status
Subject Peter Forsberg E303366 entity
Predicate memberOfSportsTeam P330 FINISHED
Object Timrå IK E628614 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: Timrå IK | Statement: [Peter Forsberg, memberOfSportsTeam, Timrå IK]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Timrå IK
Context triple: [Peter Forsberg, memberOfSportsTeam, Timrå IK]
  • A. Timrå IK chosen
    Timrå IK is a professional ice hockey club from Timrå, Sweden, best known for competing in the country’s top leagues and developing numerous elite players.
  • B. Råå IF
    Råå IF is a Swedish sports club based in the Helsingborg area, best known for its football team and long local tradition.
  • C. IFK Östersund
    IFK Östersund is a Swedish football club from Östersund, known locally for its traditional status and rivalry with Östersunds FK.
  • D. IFK Umeå
    IFK Umeå is a Swedish multi-sport club based in the city of Umeå, known for organizing and competing in various athletic disciplines.
  • E. Örgryte IS
    Örgryte IS is a Swedish sports club best known for its historic football team, one of the oldest in Sweden, based in Gothenburg.
  • 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_69d7bdef90d48190b46b88270e780946 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d961ed26588190ae76ff17159e06ec completed April 10, 2026, 8:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f671b066348190aedfe186fc4724f9 completed May 2, 2026, 9:50 p.m.
Created at: April 9, 2026, 5:22 p.m.