Triple

T12974146
Position Surface form Disambiguated ID Type / Status
Subject Compasso d’Oro E321476 entity
Predicate notableRecipient P108 FINISHED
Object Pirelli E188558 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: Pirelli | Statement: [Compasso d’Oro, notableRecipient, Pirelli]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pirelli
Context triple: [Compasso d’Oro, notableRecipient, Pirelli]
  • A. Pirelli chosen
    Pirelli is an Italian multinational company best known as one of the world’s leading manufacturers of high-performance tyres, particularly in motorsport and premium road vehicles.
  • B. Michelin
    Michelin is a major French multinational tire manufacturer renowned for its tires, travel guides, and the Michelin star restaurant rating system.
  • C. Bridgestone
    Bridgestone is a global tire and rubber company headquartered in Japan, known for its extensive involvement in motorsports and major sports sponsorships.
  • D. Pirelli DP-L10
    The Pirelli DP-L10 is a GSM mobile phone handset commonly used as a low-cost, supported platform for open-source baseband experimentation and development.
  • E. Dunlop
    Dunlop is a well-known tire manufacturer that produces high-performance motorcycle and automotive tires used in both professional racing and everyday driving.
  • 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_69d80763bd6c819094437da5b20b01d2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e4322c08190abd43daadf16097f completed April 10, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6b8ec821c81909398d8e02d69dcbf completed May 3, 2026, 2:54 a.m.
Created at: April 9, 2026, 8:37 p.m.