Triple

T8964385
Position Surface form Disambiguated ID Type / Status
Subject Bayog E214089 entity
Predicate partOf P40 FINISHED
Object Los Baños E201090 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: Los Baños | Statement: [Bayog, partOf, Los Baños]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Los Baños
Context triple: [Bayog, partOf, Los Baños]
  • A. Los Baños chosen
    Los Baños is a municipality in the Philippines known as a major center for agricultural research and education, particularly in rice science.
  • B. San Juan de Baños
    San Juan de Baños is one of the oldest surviving churches in Spain, a 7th-century Visigothic basilica renowned for its early medieval architecture and historical significance.
  • C. San Antonio de los Baños
    San Antonio de los Baños is a Cuban town known for its film school and cultural traditions, located southwest of Havana.
  • D. Malpaso
    Malpaso is the highest peak on the Canary Island of El Hierro, known for its panoramic views over the island and surrounding Atlantic Ocean.
  • E. Plaridel
    Plaridel is a municipality in the province of Bulacan in the Philippines, known for its historical significance and proximity to Metro Manila.
  • 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_69ca839cd6008190a1546a701a56710c completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc674c4be8819090d46aba8ab40af3 completed April 1, 2026, 12:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0174564188190a99e2d46a6563b69 completed April 3, 2026, 7:38 p.m.
Created at: March 30, 2026, 7:01 p.m.