Triple

T1802145
Position Surface form Disambiguated ID Type / Status
Subject Philippine government E39745 entity
Predicate hasSeatOfGovernment P761 FINISHED
Object Quezon City E10123 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: Quezon City | Statement: [Philippine government, hasSeatOfGovernment, Quezon City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Quezon City
Context triple: [Philippine government, hasSeatOfGovernment, Quezon City]
  • A. Quezon City chosen
    Quezon City is a major urban center in Metro Manila known for hosting many national government institutions, universities, and media networks in the Philippines.
  • B. Caloocan
    Caloocan is a highly urbanized city in the Philippines that forms part of the northern section of Metro Manila and serves as a major residential and commercial hub.
  • C. Metro Manila
    Metro Manila is the densely populated national capital region of the Philippines, encompassing Manila and several surrounding cities as the country’s political, economic, and cultural center.
  • D. Makati
    Makati is a highly urbanized city in Metro Manila, Philippines, known as the country’s leading financial and business center.
  • E. Mandaluyong
    Mandaluyong is a highly urbanized city in the Philippines known as part of Metro Manila’s central business and commercial district.
  • 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_69a88632aa588190ba3978fde0db5bbd completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa656c4c5481908468c6e6f9c4bfc0 completed March 6, 2026, 5:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae1fc41d248190a149252940dbdb27 completed March 9, 2026, 1:17 a.m.
Created at: March 4, 2026, 7:32 p.m.