Triple

T14090176
Position Surface form Disambiguated ID Type / Status
Subject Cotabato E339106 entity
Predicate hasMunicipality P847 FINISHED
Object Banisilan E281934 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: Banisilan | Statement: [Cotabato, hasMunicipality, Banisilan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Banisilan
Context triple: [Cotabato, hasMunicipality, Banisilan]
  • A. Banisilan chosen
    Banisilan is a landlocked agricultural municipality in the province of North Cotabato on the island of Mindanao in the Philippines.
  • B. Bayan
    Bayan is a traditional Sasak village in northern Lombok, Indonesia, known for its preserved indigenous culture, historic mosques, and role as a gateway to the Mount Rinjani area.
  • C. Ibanag
    Ibanag is an Austronesian language spoken primarily in the Cagayan Valley region of northern Luzon in the Philippines.
  • D. Malibcong
    Malibcong is a remote, mountainous municipality in the Philippine province of Abra known for its indigenous communities and largely undeveloped natural landscapes.
  • E. Bangu
    Bangu is a working-class neighborhood in the West Zone of Rio de Janeiro, Brazil, known for its hot climate, historic textile industry, and the Bangu Atlético Clube football team.
  • 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_69d81c687b0c819087fd9ed4198403f8 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de5ee3213c8190af2853a2a5b302a2 completed April 14, 2026, 3:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcd0a7aab88190949cf1fd8e11b050 completed May 7, 2026, 5:49 p.m.
Created at: April 9, 2026, 10:21 p.m.