Triple

T16360817
Position Surface form Disambiguated ID Type / Status
Subject Paombong E397304 entity
Predicate hasBarangay P29835 FINISHED
Object San Miguel E397305 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: San Miguel | Statement: [Paombong, hasBarangay, San Miguel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Miguel
Context triple: [Paombong, hasBarangay, San Miguel]
  • A. San Miguel chosen
    San Miguel is a landlocked agricultural municipality in the province of Bulacan in the Philippines, known for its historical sites and rural communities.
  • B. San Miguel
    San Miguel is a municipality located in Colombia’s southern Putumayo Department, near the border with Ecuador.
  • C. San Miguel
    San Miguel is a city in the Greater Buenos Aires metropolitan area of Argentina, located in the northwest of Buenos Aires Province.
  • D. San Miguel
    San Miguel is an active stratovolcano in eastern El Salvador, known for its frequent eruptions and prominent conical shape within the Central American volcanic chain.
  • E. San Miguel
    San Miguel is one of the defensive bastions of Fort San Pedro, a historic Spanish colonial fortification in Cebu, Philippines.
  • 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_69d87f2778dc8190aa95c7572db127e6 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2fad304448190b3f6f0350a1e151d completed April 18, 2026, 3:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a002dbce2508190b655de87f48e841e completed May 10, 2026, 7:03 a.m.
Created at: April 10, 2026, 5:08 a.m.