Triple

T1266704
Position Surface form Disambiguated ID Type / Status
Subject Tempe, Arizona E15615 entity
Predicate hasSisterCity P919 FINISHED
Object Zhenjiang, China E140148 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: Zhenjiang, China | Statement: [Tempe, Arizona, hasSisterCity, Zhenjiang, China]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zhenjiang, China
Context triple: [Tempe, Arizona, hasSisterCity, Zhenjiang, China]
  • A. Zhenjiang chosen
    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.
  • B. 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.
  • C. Wuxi
    Wuxi is a major industrial and cultural city in eastern China, located near Lake Tai and known for its manufacturing, canals, and historic gardens.
  • 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. Suzhou
    Suzhou is a historic and economically significant city in eastern China, renowned for its classical gardens, canals, and silk industry.
  • 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_69a4935a94308190bb92555b79032824 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4c037f14c8190baa42f70f8846583 completed March 1, 2026, 10:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69adc97fe2808190b421329ed239af6f completed March 8, 2026, 7:09 p.m.
Created at: March 1, 2026, 7:50 p.m.