Triple

T9241340
Position Surface form Disambiguated ID Type / Status
Subject PAL E222064 entity
Predicate operatorHeadquartersCity P62 FINISHED
Object Pasay E188579 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: Pasay | Statement: [PAL, operatorHeadquartersCity, Pasay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pasay
Context triple: [PAL, operatorHeadquartersCity, Pasay]
  • A. Pasay chosen
    Pasay is a highly urbanized coastal city in the Philippines known for its entertainment complexes, shopping centers, and proximity to Manila’s main international airport.
  • B. 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.
  • C. Tanjay
    Tanjay is a component city in the province of Negros Oriental in the Philippines, known for its agricultural economy and cultural festivals.
  • D. Plaridel
    Plaridel is a municipality in the province of Bulacan in the Philippines, known for its historical significance and proximity to Metro Manila.
  • E. Ponteareas
    Ponteareas is a municipality in the province of Pontevedra in Galicia, northwestern Spain, known for its traditional Corpus Christi flower carpets.
  • 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_69ca83ee26cc81909ac624e190597d6d completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cd03ea9d90819096f9ca5321dffd56 completed April 1, 2026, 11:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69d077e1ea448190a64a0371a412d314 completed April 4, 2026, 2:30 a.m.
Created at: March 30, 2026, 7:30 p.m.