Triple

T22202849
Position Surface form Disambiguated ID Type / Status
Subject Brøndby E548723 entity
Predicate hasPart P35 FINISHED
Object Brøndbyøster 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: Brøndbyøster | Statement: [Brøndby, hasPart, Brøndbyøster]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brøndbyøster
Context triple: [Brøndby, hasPart, Brøndbyøster]
  • A. Brøndby chosen
    Brøndby is a suburban municipality in the western part of the Copenhagen metropolitan area in Denmark, known for its residential districts and the football club Brøndby IF.
  • B. Hammersborg
    Hammersborg is a central neighborhood in Oslo, Norway, known for housing key government buildings and cultural institutions.
  • C. Fremad Amager
    Fremad Amager is a Danish football club based in the Amager district of Copenhagen that competes in the national league system.
  • D. Lyngby BK
    Lyngby BK is a Danish professional football club based in Kongens Lyngby that competes in the country’s top leagues and has developed and hosted numerous international players.
  • E. Hvidovre Fighters
    Hvidovre Fighters is a Danish professional ice hockey team based in Hvidovre that competes in the country’s top leagues.
  • 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_69e11e3ecc7c8190b5f94cd8f42e9d37 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f12b24c6fc81909e6ae62564846bd1 completed April 28, 2026, 9:48 p.m.
Created at: April 16, 2026, 8:36 p.m.