Triple

T22183767
Position Surface form Disambiguated ID Type / Status
Subject Favoriten E548236 entity
Predicate hasSportsClub P346 FINISHED
Object FK Austria Wien 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: FK Austria Wien | Statement: [Favoriten, hasSportsClub, FK Austria Wien]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: FK Austria Wien
Context triple: [Favoriten, hasSportsClub, FK Austria Wien]
  • A. FK Austria Wien chosen
    FK Austria Wien is a major Viennese football club and one of Austria’s most successful and historically significant teams.
  • B. First Vienna FC
    First Vienna FC is Austria’s oldest football club, based in Vienna and known for its historic significance in Austrian football.
  • C. SK Rapid Wien
    SK Rapid Wien is one of Austria’s most successful and popular football clubs, based in Vienna and known for its passionate fan base and historic domestic achievements.
  • D. FC Wacker Innsbruck
    FC Wacker Innsbruck is an Austrian football club based in Innsbruck, known for its participation in the country’s professional leagues and its historical presence in Tyrolean football.
  • E. FH Salzburg
    FH Salzburg is an Austrian University of Applied Sciences offering practice-oriented higher education and research programs in fields such as technology, business, design, and social sciences.
  • 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_69e11e3e0c7c8190b30d278845e2497e completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f12aa75440819084cbe9176b9edb47 completed April 28, 2026, 9:46 p.m.
Created at: April 16, 2026, 8:35 p.m.