Triple

T18434856
Position Surface form Disambiguated ID Type / Status
Subject San Lorenzo E450363 entity
Predicate near P350 FINISHED
Object Pio del Pilar 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: Pio del Pilar | Statement: [San Lorenzo, near, Pio del Pilar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pio del Pilar
Context triple: [San Lorenzo, near, Pio del Pilar]
  • A. Pio del Pilar chosen
    Pio del Pilar was a Filipino revolutionary general and key figure in the Philippine struggle for independence against Spanish colonial rule.
  • B. Fort del Pilar
    Fort del Pilar is a military reservation in Baguio City, Philippines, best known as the campus and training grounds of the Philippine Military Academy.
  • C. Our Lady of Peñafrancia
    Our Lady of Peñafrancia is a revered Marian image and patroness in the Philippines, especially venerated in the Bicol region through one of the country’s largest annual religious festivals.
  • D. El Pilar
    El Pilar is a major ancient Maya archaeological site known for its extensive ruins and surrounding protected forest on the border of Belize and Guatemala.
  • E. Virgin of Candelaria
    The Virgin of Candelaria is a revered Marian apparition and patron saint of the Canary Islands, especially venerated in Tenerife.
  • 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_69d8d381d6388190a9e94e9c658174e4 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e51c0bd35c8190b66d62ad9987377f completed April 19, 2026, 6:16 p.m.
Created at: April 10, 2026, 11:28 a.m.