Triple

T1666418
Position Surface form Disambiguated ID Type / Status
Subject Metro Manila E36022 entity
Predicate containsCity P294 FINISHED
Object Pasig E97441 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: Pasig | Statement: [Metro Manila, containsCity, Pasig]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pasig
Context triple: [Metro Manila, containsCity, Pasig]
  • A. Pasig chosen
    Pasig is a highly urbanized city in Metro Manila in the Philippines, known historically as a riverside settlement and now as a major commercial and residential center.
  • B. Pasig River
    The Pasig River is a historically significant waterway in the Philippines that flows through Metro Manila, linking Laguna de Bay to Manila Bay and serving as a central feature of the capital’s urban landscape.
  • C. Taguig
    Taguig is a highly urbanized city in Metro Manila in the Philippines, known for the Bonifacio Global City (BGC) business district and rapid commercial and residential development.
  • D. Manayunk
    Manayunk is a historic, hilly neighborhood in Northwest Philadelphia known for its Main Street shopping and dining district along the Schuylkill River.
  • E. Quezon City
    Quezon City is a major urban center in Metro Manila known for hosting many national government institutions, universities, and media networks in the 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_69a8861286808190939afff3ce8ee31e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90adc57cc8190b270004c363768e3 completed March 5, 2026, 4:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad682fd42881908ecd0f331e81aba8 completed March 8, 2026, 12:14 p.m.
Created at: March 4, 2026, 7:29 p.m.