Triple

T22560507
Position Surface form Disambiguated ID Type / Status
Subject Chivasso E557799 entity
Predicate hasNearbyAirport P4363 FINISHED
Object Turin Airport 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: Turin Airport | Statement: [Chivasso, hasNearbyAirport, Turin Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Turin Airport
Context triple: [Chivasso, hasNearbyAirport, Turin Airport]
  • A. Turin Airport chosen
    Turin Airport is the main international airport serving the city of Turin and the surrounding Piedmont region in northern Italy.
  • B. Parma Airport
    Parma Airport is a regional airport in Parma, Italy, serving domestic and limited international flights for the Emilia-Romagna region.
  • C. Cuneo International Airport
    Cuneo International Airport is a regional airport in northern Italy serving the city of Cuneo and the surrounding Piedmont area with domestic and limited international flights.
  • D. Franca Airport
    Franca Airport is a regional public airport serving the city of Franca in the state of São Paulo, Brazil.
  • E. Treviso Airport
    Treviso Airport is a small international airport in the Veneto region of northern Italy that primarily serves low-cost carriers and provides an alternative gateway to Venice.
  • 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_69e11e59db848190b4272ecd2b690ffd completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15fa5f4008190921095b7aff4f4e2 completed April 29, 2026, 1:32 a.m.
Created at: April 16, 2026, 8:52 p.m.