Triple

T12873080
Position Surface form Disambiguated ID Type / Status
Subject Aubagne E307896 entity
Predicate hasTwinTown P919 FINISHED
Object Jiangyin E384599 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: Jiangyin | Statement: [Aubagne, hasTwinTown, Jiangyin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jiangyin
Context triple: [Aubagne, hasTwinTown, Jiangyin]
  • A. Jiangyin chosen
    Jiangyin is a county-level city in Jiangsu Province, eastern China, known as an important industrial and port city 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. 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. Zhangjiagang
    Zhangjiagang is a county-level city in Jiangsu Province, China, known as a prosperous port and industrial hub along the Yangtze River.
  • E. 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.
  • 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_69d7bdf69bc48190af6c2621f28ca351 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d970f87aa48190a7234424a6badef0 completed April 10, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75d7a9e6c81908ae78daace02e7ec completed May 3, 2026, 2:36 p.m.
Created at: April 9, 2026, 5:38 p.m.