Triple

T15876362
Position Surface form Disambiguated ID Type / Status
Subject Rakhine State E384961 entity
Predicate hasCity P316 FINISHED
Object Kyaukphyu E309793 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: Kyaukphyu | Statement: [Rakhine State, hasCity, Kyaukphyu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kyaukphyu
Context triple: [Rakhine State, hasCity, Kyaukphyu]
  • A. Kyaukphyu chosen
    Kyaukphyu is a coastal town in western Myanmar that serves as a strategic deep-water port and hub for regional trade and energy projects.
  • B. Maungdaw
    Maungdaw is a town in Myanmar’s Rakhine State near the border with Bangladesh, known for its ethnically diverse population and its role in regional trade and migration.
  • C. Nyaung-U
    Nyaung-U is a historic town in central Myanmar best known as the main gateway to the ancient temple plain of Bagan.
  • D. Mangaldoi
    Mangaldoi is a town in the Indian state of Assam that serves as an important administrative and commercial center for the surrounding region.
  • E. Moulamein
    Moulamein is a small rural town in the Riverina region of New South Wales, Australia, known for its historic buildings and riverside setting.
  • 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_69d86da4e86481909f1325fdc971b5ec completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e155fdc1b881909d1c82c4c66a195a completed April 16, 2026, 9:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffb041adac8190a8e6e5c646fdedf3 completed May 9, 2026, 10:08 p.m.
Created at: April 10, 2026, 4:51 a.m.