Triple

T20292685
Position Surface form Disambiguated ID Type / Status
Subject Missionary Training Centers E510064 entity
Predicate hasLocation P40 FINISHED
Object Manila, Philippines NE NERFINISHED

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, Philippines | Statement: [Missionary Training Centers, hasLocation, Manila, Philippines]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manila, Philippines
Context triple: [Missionary Training Centers, hasLocation, Manila, Philippines]
  • A. Quezon City, Philippines
    Quezon City, Philippines is a highly urbanized city in Metro Manila that serves as a major political, educational, and commercial center of the country.
  • B. Manila
    Manila is the OpenStack shared file system service that provides scalable, API-driven management of networked file shares.
  • C. Manila
    Manila is a web-based content management and blogging system developed by UserLand Software that was popular in the early days of personal publishing on the internet.
  • D. Manila
    Manila is a fictional member of the Professor's heist crew in the Spanish television series "Money Heist" ("La Casa de Papel").
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4c652388190b782cad965e5a098 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e677024de08190bfa54ae26b5486d1 completed April 20, 2026, 6:57 p.m.
Created at: April 16, 2026, 11:12 a.m.