Triple

T20094694
Position Surface form Disambiguated ID Type / Status
Subject Marcelo Briones Fernan E496367 entity
Predicate workLocation P7 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: [Marcelo Briones Fernan, workLocation, Manila, Philippines]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manila, Philippines
Context triple: [Marcelo Briones Fernan, workLocation, Manila, Philippines]
  • A. Manila
    Manila is the OpenStack shared file system service that provides scalable, API-driven management of networked file shares.
  • B. 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.
  • C. Manila
    Manila is a fictional member of the Professor's heist crew in the Spanish television series "Money Heist" ("La Casa de Papel").
  • D. 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.
  • E. Manila, Philippine Islands
    Manila, Philippine Islands is the capital and chief city of the Philippines, serving as its historic, political, and economic 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_69da626eee3881909f3454986d4a6511 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6666b891c8190b4e4a60b73728771 completed April 20, 2026, 5:46 p.m.
Created at: April 11, 2026, 11:23 p.m.