Triple

T7541475
Position Surface form Disambiguated ID Type / Status
Subject Djurgårdens IF E178284 entity
Predicate nickname P55 FINISHED
Object Djurgårn E178284 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: Djurgårn | Statement: [Djurgårdens IF, nickname, Djurgårn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Djurgårn
Context triple: [Djurgårdens IF, nickname, Djurgårn]
  • A. Djurgården
    Djurgården is a central Stockholm island known for its parks, museums, and major attractions like the Vasa Museum and Skansen.
  • B. Djurgårdens IF chosen
    Djurgårdens IF is a prominent Swedish sports club from Stockholm, best known for its successful ice hockey and football teams and large, passionate fan base.
  • C. IFK Göteborg
    IFK Göteborg is a prominent Swedish football club based in Gothenburg, known for its domestic success and historic UEFA Cup victories.
  • D. Kalmar FF
    Kalmar FF is a professional Swedish football club based in the city of Kalmar that competes in the country’s top leagues and national competitions.
  • E. Vittsjö GIK
    Vittsjö GIK is a Swedish football club best known for its women’s team competing in the top tiers of Swedish women’s football.
  • 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_69c69f2be3888190a6667a27f8f195e9 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f8750f80819088ddfb7a5580b5df completed March 27, 2026, 9:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69c84f18e4dc81909ecd73b2b06b8d9c completed March 28, 2026, 9:58 p.m.
Created at: March 27, 2026, 3:48 p.m.