Triple

T9771783
Position Surface form Disambiguated ID Type / Status
Subject KNTV E237142 entity
Predicate studioLocation P1527 FINISHED
Object San Jose, California E1776 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 Jose, California | Statement: [KNTV, studioLocation, San Jose, California]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Jose, California
Context triple: [KNTV, studioLocation, San Jose, California]
  • A. San Jose
    San Jose is a municipality in the province of Batangas in the Philippines, known for its agricultural economy and rural communities.
  • B. San Jose
    San Jose is a coastal municipality in the Philippine province of Romblon known for its island landscapes and fishing communities.
  • C. San Jose chosen
    San Jose is a major technology and innovation hub in Silicon Valley and one of the largest cities in Northern California.
  • D. San Jose
    San Jose is a coastal municipality in the Philippine province of Negros Oriental known for its rural communities and proximity to Dumaguete City.
  • E. San Jose
    San Jose is a municipality in the province of Tarlac in the Central Luzon region of the Philippines, known for its predominantly agricultural economy.
  • 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_69ca84d831b8819090322686b47887ce completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cda0f329148190a5e531478bc18073 completed April 1, 2026, 10:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69e58a50d0748190a429af33cdced80a completed April 20, 2026, 2:07 a.m.
Created at: March 30, 2026, 8:26 p.m.