Triple

T9483423
Position Surface form Disambiguated ID Type / Status
Subject Boholano E228701 entity
Predicate region P40 FINISHED
Object Island of 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: Island of Bohol | Statement: [Boholano, region, Island of Bohol]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Island of Bohol
Context triple: [Boholano, region, Island of 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. Cebu Island
    Cebu Island is a major island in the central Philippines known for its historic city of Cebu, vibrant commerce, and popular beach and dive destinations.
  • C. Romblon Island
    Romblon Island is a small island in the central Philippines known for its marble industry, coastal landscapes, and role as the capital island of Romblon province.
  • D. Bantayan Island
    Bantayan Island is a scenic island in the central Philippines known for its white-sand beaches, clear waters, and laid-back coastal villages.
  • E. Sibuyan Island
    Sibuyan Island is a largely forested Philippine island in the Romblon province, renowned for its high endemism and the Mount Guiting-Guiting Natural Park.
  • 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_69ca84730a5081908de282651019bf2f completed March 30, 2026, 2:10 p.m.
NER Named-entity recognition batch_69cd804c859081908c261ad16b501f0d completed April 1, 2026, 8:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14bf604d4819092fc25f487866834 completed April 4, 2026, 5:35 p.m.
Created at: March 30, 2026, 7:55 p.m.