Triple

T2054362
Position Surface form Disambiguated ID Type / Status
Subject Central Visayas E45640 entity
Predicate hasProvince P285 FINISHED
Object Bohol E109173 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: Bohol | Statement: [Central Visayas, hasProvince, Bohol]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bohol
Context triple: [Central Visayas, hasProvince, Bohol]
  • A. Bohol Island chosen
    Bohol Island is a popular island province in the central Philippines known for its Chocolate Hills, tarsier sanctuaries, and white-sand beaches.
  • B. Leyte
    Leyte is a large island province in the Eastern Visayas region of the Philippines, known for its rich cultural traditions and historical significance, including major World War II events.
  • C. Romblon
    Romblon is an island province in the Philippines known for its marble industry, clear waters, and scenic beaches.
  • D. Guimaras
    Guimaras is a small island province in the Philippines known for its mango production, coastal scenery, and predominantly Hiligaynon-speaking population.
  • E. Palawan
    Palawan is a large island province in the western Philippines known for its stunning limestone cliffs, clear turquoise waters, rich marine biodiversity, and popular ecotourism destinations like El Nido and Puerto Princesa.
  • 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_69a8891a19508190a12ef1e192308dcb completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb9a8518081909ba95a8ef9321f12 completed March 7, 2026, 5:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae200eb09881908bbfe47ebb62f55e completed March 9, 2026, 1:19 a.m.
Created at: March 4, 2026, 7:39 p.m.