Triple

T9522078
Position Surface form Disambiguated ID Type / Status
Subject Shan State E229667 entity
Predicate containsCity P294 FINISHED
Object Lashio E97789 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: Lashio | Statement: [Shan State, containsCity, Lashio]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lashio
Context triple: [Shan State, containsCity, Lashio]
  • A. Lashio chosen
    Lashio is a key town in northern Myanmar that historically served as an important transport and trade hub, particularly during World War II as the inland gateway to the Burma Road.
  • B. Mawlamyine
    Mawlamyine is a coastal city in southeastern Myanmar and the capital of Mon State, known historically as an important port and cultural center.
  • C. Pathein
    Pathein is a major city in Myanmar’s Ayeyarwady Region, known as a regional commercial hub and for its traditional handcrafted umbrellas.
  • D. Amarapura
    Amarapura is a former royal city in Myanmar renowned for its role as an early Burmese capital and for landmarks such as the U Bein Bridge.
  • 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_69ca847870a881909d8d751a7d29da39 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd989788e4819086c235bf37a56b04 completed April 1, 2026, 10:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1612f86ac8190a19a2d17b25c08e6 completed April 4, 2026, 7:06 p.m.
Created at: March 30, 2026, 7:59 p.m.