Triple

T19714232
Position Surface form Disambiguated ID Type / Status
Subject Suzhou Municipal People’s Government E473430 entity
Predicate governs P760 FINISHED
Object Zhangjiagang NE NERFINISHED

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: Zhangjiagang | Statement: [Suzhou Municipal People’s Government, governs, Zhangjiagang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zhangjiagang
Context triple: [Suzhou Municipal People’s Government, governs, Zhangjiagang]
  • A. Zhangjiagang chosen
    Zhangjiagang is a county-level city in Jiangsu Province, China, known as a prosperous port and industrial hub along the Yangtze River.
  • B. Zhenjiang
    Zhenjiang is a historic port city in eastern China known for its strategic location on the Yangtze River and its rich cultural and culinary heritage.
  • C. Changzhou
    Changzhou is a major industrial and commercial city in Jiangsu Province, eastern China, known for its manufacturing base and location along the Yangtze River.
  • D. Nantong
    Nantong is a coastal city in eastern China known for its textile industry, river and sea ports, and location on the northern bank of the Yangtze River opposite Shanghai.
  • E. Kunshan
    Kunshan is a rapidly developing county-level city in Jiangsu Province, China, known for its strong manufacturing economy and proximity to Shanghai and Suzhou.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e516dd048190a0b6c93ea3e71f58 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e6440b47508190a8a33325b00841dc completed April 20, 2026, 3:19 p.m.
Created at: April 10, 2026, 1:46 p.m.