Triple

T8242573
Position Surface form Disambiguated ID Type / Status
Subject Narita E192571 entity
Predicate hasSisterCity P919 FINISHED
Object Wuxi, Jiangsu, China 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, Jiangsu, China | Statement: [Narita, hasSisterCity, Wuxi, Jiangsu, China]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wuxi, Jiangsu, China
Context triple: [Narita, hasSisterCity, Wuxi, Jiangsu, China]
  • 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. Taicang, Jiangsu
    Taicang, Jiangsu is a county-level city under Suzhou in eastern China, known for its developed port on the Yangtze River estuary and its strong manufacturing and logistics industries.
  • 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. Jiangyin
    Jiangyin is a county-level city in Jiangsu Province, eastern China, known as an important industrial and port city along the Yangtze River.
  • E. Hsiangcheng, China
    Hsiangcheng, China is a town in Henan Province known as the birthplace of author and social critic Os Guinness.
  • 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_69ca82dc8f148190a2c75a98501a7b91 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb786f65708190a92ec282b280c813 completed March 31, 2026, 7:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd351871ac81909f8e4a72a6b99ac3 completed April 1, 2026, 3:09 p.m.
Created at: March 30, 2026, 5:47 p.m.