Triple

T14354092
Position Surface form Disambiguated ID Type / Status
Subject South Bay Shores E355926 entity
Predicate city P40 FINISHED
Object Santa Clara E13690 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 Clara | Statement: [South Bay Shores, city, Santa Clara]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Santa Clara
Context triple: [South Bay Shores, city, Santa Clara]
  • A. Santa Clara chosen
    Santa Clara is a Silicon Valley city in California known for its high-tech industry presence, Levi’s Stadium, and Santa Clara University.
  • B. Santa Clara
    Santa Clara is a major city in central Cuba known as the capital of Villa Clara Province and a historic site of key battles in the Cuban Revolution.
  • C. Santa Clara
    Santa Clara is a settlement located within the Arraiján District in Panama.
  • D. San Mateo
    San Mateo is a landlocked municipality in the province of Rizal in the Philippines, known for its mix of suburban communities, hilly terrain, and proximity to Metro Manila.
  • E. San Mateo
    San Mateo is a city in California’s San Francisco Bay Area, known for its suburban neighborhoods, parks, and role as a commercial and residential hub on the Peninsula.
  • 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_69d82790a7e08190877e2d349b2e8d8e completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de8f4ff1e48190bd9419d70098cede completed April 14, 2026, 7:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd7a3479088190929ab4b9d218a608 completed May 8, 2026, 5:52 a.m.
Created at: April 10, 2026, 1:14 a.m.