Triple

T10611138
Position Surface form Disambiguated ID Type / Status
Subject Negros Occidental E276009 entity
Predicate hasCity P316 FINISHED
Object Sipalay E298770 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: Sipalay | Statement: [Negros Occidental, hasCity, Sipalay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sipalay
Context triple: [Negros Occidental, hasCity, Sipalay]
  • A. Sipalay chosen
    Sipalay is a coastal city in Negros Occidental, Philippines, known for its beaches, diving spots, and laid-back tourism.
  • B. Bamban
    Bamban is a municipality in the province of Tarlac in the Philippines, known for its proximity to Mount Pinatubo and its role in the region’s post-eruption development and eco-tourism.
  • C. Dipaculao
    Dipaculao is a coastal municipality in the Philippine province of Aurora known for its beaches, surfing spots, and scenic mountain landscapes.
  • D. Balamban
    Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
  • E. Sagay
    Sagay is a coastal city in the province of Negros Occidental in the Philippines, known for its rich marine resources and protected seascape.
  • 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_69d6aaf948d88190806cc3a8c47a3fb2 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d6df5a1450819082ad445712fb7868 completed April 8, 2026, 11:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69d95ec61b388190adee9b3577567709 completed April 10, 2026, 8:34 p.m.
Created at: April 8, 2026, 7:33 p.m.