Triple

T6950002
Position Surface form Disambiguated ID Type / Status
Subject GSIS Building, Pasay, Metro Manila E160894 entity
Predicate city P40 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: [GSIS Building, Pasay, Metro Manila, city, Pasay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pasay
Context triple: [GSIS Building, Pasay, Metro Manila, city, 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_69c68850419081909fb426b8f5a304c7 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6daaeb66c8190a62b32a2c22f166a completed March 27, 2026, 7:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7618876948190abda9cb234bbe225 completed March 28, 2026, 5:05 a.m.
Created at: March 27, 2026, 2:29 p.m.