Triple

T16427597
Position Surface form Disambiguated ID Type / Status
Subject Obando E398983 entity
Predicate locatedNear P294 FINISHED
Object Meycauayan 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: Meycauayan | Statement: [Obando, locatedNear, Meycauayan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Meycauayan
Context triple: [Obando, locatedNear, Meycauayan]
  • A. Meycauayan chosen
    Meycauayan is a highly urbanized city in the Philippine province of Bulacan known for its jewelry and leather industries.
  • B. Tagaytay City
    Tagaytay City is a popular highland tourist destination in the Philippines known for its cool climate and scenic views of Taal Volcano and Taal Lake.
  • C. Abucay
    Abucay is a coastal municipality in the province of Bataan in the Philippines, known for its historical significance dating back to the Spanish colonial period.
  • D. Mabalacat
    Mabalacat is a city in the Philippine province of Pampanga known for hosting part of Clark Freeport and Special Economic Zone, a major commercial and aviation hub.
  • E. Cabanatuan City
    Cabanatuan City is a highly urbanized commercial and transportation hub in the Philippine province of Nueva Ecija, historically known as the "Tricycle Capital of the 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_69d87f2b9024819085c20e52de95d583 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e328fc223c8190bbed29907351a6f6 completed April 18, 2026, 6:47 a.m.
Created at: April 10, 2026, 5:09 a.m.