Triple

T7255230
Position Surface form Disambiguated ID Type / Status
Subject Homma trial E157701 entity
Predicate location P40 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: [Homma trial, location, Manila]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manila
Context triple: [Homma trial, location, 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. San Miguel, Manila
    San Miguel, Manila is a historic district in the city of Manila, Philippines, known for housing the presidential Malacañang Palace and various government and educational institutions.
  • C. 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.
  • D. 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.
  • E. Makati
    Makati is a highly urbanized city in Metro Manila, Philippines, known as the country’s leading financial and business center.
  • 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_69c6882d81d4819085f7ff862951ee4f completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6eaa0c76c81909fe43ed6938a13ea completed March 27, 2026, 8:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7db14f6c481908084aaa49d82787d completed March 28, 2026, 1:43 p.m.
Created at: March 27, 2026, 2:56 p.m.