Triple

T8161839
Position Surface form Disambiguated ID Type / Status
Subject Laguna E190593 entity
Predicate hasCity P316 FINISHED
Object Cabuyao E432922 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: Cabuyao | Statement: [Laguna, hasCity, Cabuyao]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cabuyao
Context triple: [Laguna, hasCity, Cabuyao]
  • A. Cabuyao chosen
    Cabuyao is a rapidly developing industrial and residential city in the province of Laguna on the island of Luzon in the Philippines.
  • B. Mabalacat
    Mabalacat is a city in the Philippine province of Pampanga known for hosting part of Clark Freeport and Special Economic Zone, a major commercial and aviation hub.
  • C. Abucay
    Abucay is a coastal municipality in the province of Bataan in the Philippines, known for its historical significance dating back to the Spanish colonial period.
  • D. Cabanatuan City
    Cabanatuan City is a highly urbanized commercial and transportation hub in the Philippine province of Nueva Ecija, historically known as the "Tricycle Capital of the Philippines."
  • E. Sipalay
    Sipalay is a coastal city in Negros Occidental, Philippines, known for its beaches, diving spots, and laid-back tourism.
  • 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_69ca82c0ef14819083713f4473dd847c completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb4556b45c819089eb15ad027b036a completed March 31, 2026, 3:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69ccbf2c22f0819085c686c005f49486 completed April 1, 2026, 6:46 a.m.
Created at: March 30, 2026, 5:38 p.m.