Triple

T14016617
Position Surface form Disambiguated ID Type / Status
Subject Governor-General of Huguang E337219 entity
Predicate seatOfGovernment P761 FINISHED
Object Wuchang E1680 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: Wuchang | Statement: [Governor-General of Huguang, seatOfGovernment, Wuchang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wuchang
Context triple: [Governor-General of Huguang, seatOfGovernment, Wuchang]
  • A. Wuhan chosen
    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.
  • B. Huangshi
    Huangshi is an industrial city in eastern Hubei Province, China, known for its steel production and location along the Yangtze River.
  • C. Huangzhou
    Huangzhou is the central urban district and administrative heart of Huanggang in Hubei Province, China.
  • D. Huguang
    Huguang was a historical administrative region of imperial China that roughly encompassed the areas of modern Hubei and Hunan provinces.
  • E. Zhongdu
    Zhongdu was the historical capital city of the Jurchen-led Jin dynasty in northern China, located in what is now part of modern Beijing.
  • 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_69d81c6543a48190bd5ba93d7419e797 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2f396b648190927e5718c3bb6511 completed April 14, 2026, 12:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcd095ca5081908d7fed82e9ef0252 completed May 7, 2026, 5:49 p.m.
Created at: April 9, 2026, 10:19 p.m.