Triple

T8734595
Position Surface form Disambiguated ID Type / Status
Subject VfL Wolfsburg E207343 entity
Predicate majorSponsor P1807 FINISHED
Object Volkswagen E6000 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: Volkswagen | Statement: [VfL Wolfsburg, majorSponsor, Volkswagen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Volkswagen
Context triple: [VfL Wolfsburg, majorSponsor, Volkswagen]
  • A. Volkswagen Group chosen
    Volkswagen Group is a major German multinational automotive manufacturer that owns brands such as Volkswagen, Audi, Porsche, and Škoda and is one of the largest car producers in the world.
  • B. Audi
    Audi is a German luxury automobile manufacturer known for its premium vehicles, advanced engineering, and signature quattro all-wheel-drive technology.
  • C. Daimler
    Daimler is a historic British luxury automobile manufacturer renowned for producing high-end saloon cars and limousines, particularly favored by royalty and official state fleets.
  • D. Porsche
    Porsche is a German luxury automobile manufacturer renowned for its high-performance sports cars, SUVs, and engineering excellence.
  • E. Volkswagen Truck & Bus
    Volkswagen Truck & Bus is a commercial vehicle manufacturer within the Volkswagen Group, known for producing trucks and buses for global markets.
  • 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_69ca8358e4008190898471a59b96c301 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5d2b89988190bb7671e273026046 completed March 31, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69d0c696913c819089f29ec899d4ee61 completed April 4, 2026, 8:06 a.m.
Created at: March 30, 2026, 6:37 p.m.