Triple

T1522282
Position Surface form Disambiguated ID Type / Status
Subject University of Medicine 2, Yangon E32255 entity
Predicate city P40 FINISHED
Object Yangon E42684 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: Yangon | Statement: [University of Medicine 2, Yangon, city, Yangon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yangon
Context triple: [University of Medicine 2, Yangon, city, Yangon]
  • A. Yangon chosen
    Yangon is Myanmar’s largest city and former capital, known as a major commercial hub featuring a mix of colonial architecture and prominent Buddhist landmarks like the Shwedagon Pagoda.
  • B. Mandalay
    Mandalay is a major cultural and economic center in central Myanmar, historically known as the last royal capital of the Burmese kingdom.
  • C. Lashio
    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.
  • D. Chiang Mai
    Chiang Mai is a historic city in northern Thailand known for its ancient temples, vibrant night markets, and surrounding mountainous landscapes.
  • E. Bangkok
    Bangkok is the vibrant capital and largest city of Thailand, known for its bustling street life, ornate temples, and role as a major economic and cultural hub in Southeast Asia.
  • 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_69a885e9b0ac819093a9806ad0efc82c completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a907fe8b0c8190a765afd3a10ee5e0 completed March 5, 2026, 4:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad294f9e2481909f1d685d7f083c6a completed March 8, 2026, 7:46 a.m.
Created at: March 4, 2026, 7:26 p.m.