Triple

T15901977
Position Surface form Disambiguated ID Type / Status
Subject Sambali E385610 entity
Predicate region P40 FINISHED
Object Zambales Province NE NERFINISHED

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: Zambales Province | Statement: [Sambali, region, Zambales Province]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zambales Province
Context triple: [Sambali, region, Zambales Province]
  • A. Zambales chosen
    Zambales is a coastal province in the Central Luzon region of the Philippines, known for its beaches, mangoes, and ethnolinguistic diversity.
  • B. Province of Zambales
    The Province of Zambales is a coastal province in Central Luzon, Philippines, known for its beaches, coves, and the former U.S. naval base at Subic Bay.
  • C. Pangasinan
    Pangasinan is a populous coastal province in the Philippines known for its rich Ilocano and Pangasinense culture, agriculture, and tourism sites such as the Hundred Islands National Park.
  • D. Pangasinan
    Pangasinan is an Austronesian language spoken primarily in the Pangasinan province and surrounding areas of northwestern Luzon in the Philippines.
  • E. Rizal province
    Rizal province is a landlocked province in the Calabarzon region of Luzon in the Philippines, known for its proximity to Metro Manila and its mix of suburban communities, industrial areas, and scenic highlands.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d86da5b800819083a31be937d738b0 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1563db01081908dd94a2536ef2107 completed April 16, 2026, 9:35 p.m.
Created at: April 10, 2026, 4:52 a.m.