Triple

T10215990
Position Surface form Disambiguated ID Type / Status
Subject Miguel de Benavides E242441 entity
Predicate notableWorkLocation P3858 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: [Miguel de Benavides, notableWorkLocation, Manila]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manila
Context triple: [Miguel de Benavides, notableWorkLocation, 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. Manila
    Manila is the OpenStack shared file system service that provides scalable, API-driven management of networked file shares.
  • C. 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.
  • D. 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.
  • E. 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.
  • 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_69d381ae26c48190985abd0e25ee5d04 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d3aa2894d0819095704449ecc2db6c completed April 6, 2026, 12:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d6f6f4d0cc8190ae41277b15b3012d completed April 9, 2026, 12:46 a.m.
Created at: April 6, 2026, 11:05 a.m.