Triple

T8009429
Position Surface form Disambiguated ID Type / Status
Subject Santa Maria Public Airport E186445 entity
Predicate hasCityServed P3936 FINISHED
Object Santa Maria E147373 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 Maria | Statement: [Santa Maria Public Airport, hasCityServed, Santa Maria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Santa Maria
Context triple: [Santa Maria Public Airport, hasCityServed, Santa Maria]
  • A. Santa Maria chosen
    Santa Maria is a city in California’s Central Coast region known for its agriculture, wine industry, and distinctive Santa Maria–style barbecue.
  • B. Santa Maria
    Santa Maria is a popular sandy beach on the Greek island of Paros, known for its clear waters, water sports, and lively summer atmosphere.
  • C. Santa Maria
    Santa Maria is a first-class, landlocked municipality in the province of Bulacan in the Philippines, known for its rapidly growing suburban communities and proximity to Metro Manila.
  • D. Santa Maria
    Santa Maria is a coastal town on the southern tip of Sal Island in Cape Verde, known for its sandy beaches, tourism, and water sports.
  • E. Santa Maria
    Santa Maria is an administrative region within Brazil's Federal District, functioning as part of the greater Brasília metropolitan area.
  • 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_69ca82abaffc8190ab8af79cdbc31ab3 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3d6f76408190a1312369521a187a completed March 31, 2026, 3:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc569e90d48190a1bf1495496017f8 completed March 31, 2026, 11:19 p.m.
Created at: March 30, 2026, 5:19 p.m.