Triple

T8151499
Position Surface form Disambiguated ID Type / Status
Subject Michael Laudrup E190343 entity
Predicate memberOfSportsTeam P330 FINISHED
Object Brøndby IF E548723 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: Brøndby IF | Statement: [Michael Laudrup, memberOfSportsTeam, Brøndby IF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brøndby IF
Context triple: [Michael Laudrup, memberOfSportsTeam, Brøndby IF]
  • 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. Odense Boldklub
    Odense Boldklub is a Danish professional football club based in the city of Odense, known for competing in the top tiers of Danish football.
  • C. FC Midtjylland
    FC Midtjylland is a Danish professional football club known for its data-driven approach to player recruitment and performance, competing in the top tier of Danish football.
  • D. Hammersborg
    Hammersborg is a central neighborhood in Oslo, Norway, known for housing key government buildings and cultural institutions.
  • E. Rosenborg BK
    Rosenborg BK is a Norwegian professional football club from Trondheim, historically one of the country’s most successful teams and a dominant force in the Eliteserien.
  • 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_69ca82be7ba8819087de0147e9292c83 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb4483799c81908e73f9a87ed99185 completed March 31, 2026, 3:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69ccbeef189081909f45b8d2aea74225 completed April 1, 2026, 6:45 a.m.
Created at: March 30, 2026, 5:37 p.m.