Triple

T23488597
Position Surface form Disambiguated ID Type / Status
Subject Myanmar Police Force E570608 entity
Predicate headquartersLocation P62 FINISHED
Object Naypyidaw 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: Naypyidaw | Statement: [Myanmar Police Force, headquartersLocation, Naypyidaw]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Naypyidaw
Context triple: [Myanmar Police Force, headquartersLocation, Naypyidaw]
  • A. Naypyidaw chosen
    Naypyidaw is Myanmar’s planned administrative city known for its vast, sparsely populated layout and role as the country’s political center.
  • B. Nayapala
    Nayapala was a ruler of the Pala dynasty in eastern India, known for consolidating Pala power in Bengal and Bihar during the 11th century.
  • C. Nabaoy
    Nabaoy is a rural barangay in the Municipality of Malay, Aklan, Philippines, known for its river, eco-tourism activities, and natural scenery.
  • D. Nawalapitiya
    Nawalapitiya is a town in Sri Lanka known for its tea plantations and hilly terrain, located within the country's Central Province.
  • E. Nay
    Nay is a small commune in southwestern France’s Pyrénées-Atlantiques department, known for its historic town center and location near the Pyrenees.
  • 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_69e245b0b01481908f636939bedd804c completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1a7d9cc08819084c532b069f867ee completed April 29, 2026, 6:40 a.m.
Created at: April 17, 2026, 6:04 p.m.