Triple

T9144892
Position Surface form Disambiguated ID Type / Status
Subject ancient State of Sui E219427 entity
Predicate locatedIn P40 FINISHED
Object Suizhou E42779 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: Suizhou | Statement: [ancient State of Sui, locatedIn, Suizhou]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Suizhou
Context triple: [ancient State of Sui, locatedIn, Suizhou]
  • A. Suizhou chosen
    Suizhou is a county-level city in northern Hubei Province, China, known for its historical sites and role as a regional transport and economic hub.
  • B. Ezhou
    Ezhou is a prefecture-level city in eastern Hubei Province, China, known for its location along the Yangtze River and its growing role as a regional transportation and industrial hub.
  • C. Guangshui
    Guangshui is a county-level city in central China's Hubei province, known for its historical sites and role as a regional transportation hub.
  • D. Jingmen
    Jingmen is a prefecture-level city in central China known for its role as a regional industrial and transportation hub within Hubei Province.
  • E. Xiangyang
    Xiangyang is a historic prefecture-level city in northern Hubei Province, China, known for its strategic location on the Han River and well-preserved ancient city walls.
  • 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_69ca83e121dc81909912bd66953081c5 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cca9166d308190a742ae68371439ee completed April 1, 2026, 5:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2696450e4819086d5aac368127e5f completed April 5, 2026, 1:53 p.m.
Created at: March 30, 2026, 7:19 p.m.