Triple

T2677744
Position Surface form Disambiguated ID Type / Status
Subject MRT Line 3 E56499 entity
Predicate cityServed P82 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: [MRT Line 3, cityServed, Pasay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pasay
Context triple: [MRT Line 3, cityServed, 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. Los Baños
    Los Baños is a municipality in the Philippines known as a major center for agricultural research and education, particularly in rice science.
  • E. Las Piñas
    Las Piñas is a highly urbanized city in the southern part of Metro Manila in the Philippines, known for its residential communities and the historic Bamboo Organ.
  • 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_69ab4a4b13fc81909dfdb3f23da46832 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd9b697fc8190a5ec8b75ee2ad238 completed March 7, 2026, 7:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69afa065a6f48190973a3b6c52aa23bf completed March 10, 2026, 4:39 a.m.
Created at: March 6, 2026, 9:54 p.m.