Triple

T3270929
Position Surface form Disambiguated ID Type / Status
Subject Biliran Island E68645 entity
Predicate hasMunicipality P847 FINISHED
Object Biliran E342589 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: Biliran | Statement: [Biliran Island, hasMunicipality, Biliran]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Biliran
Context triple: [Biliran Island, hasMunicipality, Biliran]
  • A. Biliran
    Biliran is an island province in the central Philippines known for its volcanic landscapes, waterfalls, and coastal scenery.
  • B. Sibulan
    Sibulan is a coastal municipality in the Philippine province of Negros Oriental known as a gateway to Dumaguete City and for its local airport and seaport.
  • C. Bislig
    Bislig is a coastal city in the Caraga region of Mindanao in the Philippines, known for its proximity to the Tinuy-an Falls and its history as a former major paper-mill town.
  • D. Balangiga
    Balangiga is a coastal municipality in the province of Eastern Samar in the Philippines, historically known for the 1901 Balangiga encounter during the Philippine–American War.
  • E. Biliran Province chosen
    Biliran Province is a small island province in the Eastern Visayas region of the Philippines, known for its volcanic landscapes, waterfalls, and coastal scenery.
  • 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_69ad859b54f881909bf530d549caf2fd completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adaff4b9dc8190b7e3da0bbffccf99 completed March 8, 2026, 5:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2e83e11f081909d64287c0902124a completed March 12, 2026, 4:22 p.m.
Created at: March 8, 2026, 3:09 p.m.