Triple

T14010915
Position Surface form Disambiguated ID Type / Status
Subject Sauce Viejo Airport E337075 entity
Predicate cityServed P82 FINISHED
Object Santa Fe E592450 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: Santa Fe | Statement: [Sauce Viejo Airport, cityServed, Santa Fe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Santa Fe
Context triple: [Sauce Viejo Airport, cityServed, Santa Fe]
  • A. Santa Fe
    Santa Fe is a town on Cuba’s Isla de la Juventud, known as one of the island’s principal local settlements.
  • B. Santa Fe chosen
    Santa Fe is the capital city of New Mexico, known for its Pueblo-style architecture, vibrant arts scene, and rich blend of Native American and Spanish colonial history.
  • C. Santa Fe
    Santa Fe is a major modern business and financial district in western Mexico City known for its corporate offices, upscale shopping centers, and contemporary high-rise architecture.
  • D. Santa Fe
    Santa Fe was a major American railroad company that played a key role in the development and transportation infrastructure of the western United States.
  • E. Santa Fe
    Santa Fe is a small coastal municipality in the island province of Romblon in the Philippines, known for its rural communities and surrounding marine scenery.
  • 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_69d81c645c5c8190b1fd16a285a1b78a completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2ed5cfd0819085b9c860b119a9de completed April 14, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdd5b98d6881908c0efd086a973af2 completed May 8, 2026, 12:23 p.m.
Created at: April 9, 2026, 10:19 p.m.