Triple

T18946719
Position Surface form Disambiguated ID Type / Status
Subject Sibuyan Island E463532 entity
Predicate hasMunicipality P847 FINISHED
Object Magdiwang 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: Magdiwang | Statement: [Sibuyan Island, hasMunicipality, Magdiwang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Magdiwang
Context triple: [Sibuyan Island, hasMunicipality, Magdiwang]
  • A. Magdiwang chosen
    Magdiwang is a coastal municipality located on Sibuyan Island in the province of Romblon in the Philippines.
  • B. Guindulungan
    Guindulungan is a municipality in the province of Maguindanao in the Bangsamoro Autonomous Region in Muslim Mindanao in the southern Philippines.
  • C. Magsingal
    Magsingal is a coastal municipality in the province of Ilocos Sur in the Philippines, known for its agricultural lands and historic church.
  • D. Babatngon
    Babatngon is a coastal municipality in the province of Leyte in the Philippines, known for its fishing industry and rural communities.
  • E. Timugan
    Timugan is a barangay (village-level administrative division) in the municipality of Los Baños in the province of Laguna, Philippines.
  • 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_69d8dcfec90481909e926be9767e5779 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5d5402ad881908add559249278895 completed April 20, 2026, 7:26 a.m.
Created at: April 10, 2026, 11:59 a.m.