Triple

T301403
Position Surface form Disambiguated ID Type / Status
Subject Hanoi E6204 entity
Predicate formerName P65 FINISHED
Object Đông Kinh E48177 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: Đông Kinh | Statement: [Hanoi, formerName, Đông Kinh]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Đông Kinh
Context triple: [Hanoi, formerName, Đông Kinh]
  • A. Đông Đô chosen
    Đông Đô was a historical name for the city now known as Hanoi, which has long served as a major political and cultural center of Vietnam.
  • B. Nanjing
    Nanjing is a major city in eastern China, historically significant as a former national capital and cultural center, and now an important political, economic, and educational hub on the Yangtze River.
  • C. Huangshi
    Huangshi is an industrial city in eastern Hubei Province, China, known for its steel production and location along the Yangtze River.
  • D. Wuhan
    Wuhan is a major city in central China, known as a key industrial, commercial, and transportation hub located at the confluence of the Yangtze and Han rivers.
  • E. Shenyang
    Shenyang is a major industrial and historical city in northeastern China and the capital of Liaoning Province.
  • 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_69a2e79230508190b912ecb555aae17e completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2e9e6a8308190b9bd15310e324504 completed Feb. 28, 2026, 1:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3fe8dde4c8190bd897e5251eb6941 completed March 1, 2026, 8:53 a.m.
Created at: Feb. 28, 2026, 1:06 p.m.