Triple

T4927760
Position Surface form Disambiguated ID Type / Status
Subject Giorgetto Giugiaro E110617 entity
Predicate designedFor P98 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: [Giorgetto Giugiaro, designedFor, Volkswagen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Volkswagen
Context triple: [Giorgetto Giugiaro, designedFor, 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. Porsche
    Porsche is a German luxury automobile manufacturer renowned for its high-performance sports cars, SUVs, and engineering excellence.
  • D. Volkswagen Truck & Bus
    Volkswagen Truck & Bus is a commercial vehicle manufacturer within the Volkswagen Group, known for producing trucks and buses for global markets.
  • E. Volkswagen Commercial Vehicles
    Volkswagen Commercial Vehicles is a division of the Volkswagen Group specializing in the development, production, and sale of light commercial vehicles such as vans and pickups for business and private use.
  • 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_69bd4415190c8190817bee7ec9f9f944 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd7036d8e88190bc4be2975160da23 completed March 20, 2026, 4:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69be77ac88148190a51fa2e9085d6897 completed March 21, 2026, 10:49 a.m.
Created at: March 20, 2026, 1:30 p.m.