Triple

T15087768
Position Surface form Disambiguated ID Type / Status
Subject Jiancaoping District E360325 entity
Predicate locatedIn P40 FINISHED
Object Taiyuan E80116 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: Taiyuan | Statement: [Jiancaoping District, locatedIn, Taiyuan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taiyuan
Context triple: [Jiancaoping District, locatedIn, Taiyuan]
  • A. Taiyuan chosen
    Taiyuan is the capital and largest city of Shanxi Province in northern China, known as an important industrial and transportation hub with a long imperial history.
  • B. Taiyuan
    Taiyuan was a historical Chinese era name used during the reign of the Eastern Jin emperor Sun Liang.
  • C. Xinzhou
    Xinzhou is a prefecture-level city in northern China known for its historical sites and location within Shanxi Province’s coal-rich and culturally significant region.
  • D. Datong
    Datong is a historic industrial city in northern China known for its coal production and nearby cultural landmarks such as the Yungang Grottoes.
  • E. Jinzhong
    Jinzhong is a prefecture-level city in northern China known for its historical sites and cultural heritage within Shanxi Province.
  • 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_69d85a035aa88190b52a139d3a1b7b6d completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00276d1608190bc310d5b86ecd1d5 completed April 15, 2026, 9:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69feae1ba4208190b1e8c55668a1b422 completed May 9, 2026, 3:46 a.m.
Created at: April 10, 2026, 3:03 a.m.