Triple

T1666437
Position Surface form Disambiguated ID Type / Status
Subject Metro Manila E36022 entity
Predicate contains P35 FINISHED
Object Malacañang Palace E84672 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: Malacañang Palace | Statement: [Metro Manila, contains, Malacañang Palace]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Malacañang Palace
Context triple: [Metro Manila, contains, Malacañang Palace]
  • A. Malacañang Palace chosen
    Malacañang Palace is the historic official residence and principal workplace of the President of the Philippines, located along the Pasig River in Manila.
  • B. Batasang Pambansa Complex
    The Batasang Pambansa Complex is the main legislative center of the Philippines, serving as the seat of its House of Representatives.
  • C. Old Legislative Building, Manila
    The Old Legislative Building in Manila is a historic neoclassical structure that long served as the seat of the Philippine legislature and now houses the National Museum of Fine Arts.
  • D. Quezon City Hall
    Quezon City Hall is the main government complex and administrative center of Quezon City in Metro Manila, Philippines.
  • E. Presidential Palace
    The Presidential Palace in Nanjing is a historic government complex that served as the headquarters of several Chinese regimes, most notably the Republic of China before 1949.
  • 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_69a8861286808190939afff3ce8ee31e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90adc57cc8190b270004c363768e3 completed March 5, 2026, 4:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad682fd42881908ecd0f331e81aba8 completed March 8, 2026, 12:14 p.m.
Created at: March 4, 2026, 7:29 p.m.