Triple

T363039
Position Surface form Disambiguated ID Type / Status
Subject Manila E7896 entity
Predicate shortName P43 FINISHED
Object Manila E7896 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: Manila | Statement: [Manila, shortName, Manila]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manila
Context triple: [Manila, shortName, Manila]
  • A. Manila chosen
    Manila is the capital city of the Philippines, a historic and densely populated coastal metropolis that has long served as the country’s political, economic, and cultural center.
  • B. 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.
  • 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. Cebu
    Cebu is a major island and province in the central Philippines known for its historic role in Spanish colonization, vibrant urban center Cebu City, and popular beach and dive tourism.
  • E. Los Baños, Laguna, Philippines
    Los Baños, Laguna, Philippines is a university town and science hub on the island of Luzon known for its agricultural research institutions, hot springs, and proximity to Mount Makiling.
  • 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_69a2e7e880008190a6ad7e06e5d03007 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ebcfb0f48190b9a9010c7837ac58 completed Feb. 28, 2026, 1:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3f0a5e5dc81909363a31e9b713368 completed March 1, 2026, 7:54 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.