Triple

T11715663
Position Surface form Disambiguated ID Type / Status
Subject San Rafael E278489 entity
Predicate hasAirport P105 FINISHED
Object San Rafael Airport E724654 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: San Rafael Airport | Statement: [San Rafael, hasAirport, San Rafael Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Rafael Airport
Context triple: [San Rafael, hasAirport, San Rafael Airport]
  • A. San Rafael Airport chosen
    San Rafael Airport is a regional public airport serving the city of San Rafael in Mendoza Province, Argentina.
  • B. Santa Rosa Airport
    Santa Rosa Airport is a regional public airport serving the city of Santa Rosa in La Pampa Province, Argentina.
  • C. Del Norte International Airport
    Del Norte International Airport is a regional airport serving the Monterrey metropolitan area in the state of Nuevo León, Mexico.
  • D. Del Norte County Regional Airport
    Del Norte County Regional Airport is a public airport serving the air transportation needs of Crescent City and the surrounding Del Norte County region in far northern California.
  • E. Monterey Regional Airport
    Monterey Regional Airport is a public airport on California’s central coast that provides commercial air service to the Monterey Bay area and surrounding communities.
  • 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_69d6aaff2ce88190b4a1e4b341ad5377 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a4bf54d88190a8e07fbbf8d9e962 completed April 10, 2026, 7:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef8397a4ac8190a71dfdd53bfa168a completed April 27, 2026, 3:41 p.m.
Created at: April 8, 2026, 9:40 p.m.