Triple

T2422576
Position Surface form Disambiguated ID Type / Status
Subject Shenzhen Airlines E53450 entity
Predicate focusCity P164 FINISHED
Object Wuxi E133333 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: Wuxi | Statement: [Shenzhen Airlines, focusCity, Wuxi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wuxi
Context triple: [Shenzhen Airlines, focusCity, Wuxi]
  • A. Wuxi chosen
    Wuxi is a major industrial and cultural city in eastern China, located near Lake Tai and known for its manufacturing, canals, and historic gardens.
  • 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. 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.
  • 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_69ab495c44d48190b7235b23719bc3f6 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc972934481909e05bd6f31162f9d completed March 7, 2026, 6:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69b12ded5f9c8190a0de21b631d970b0 completed March 11, 2026, 8:55 a.m.
Created at: March 6, 2026, 9:42 p.m.