Triple

T9397374
Position Surface form Disambiguated ID Type / Status
Subject Xiaochang County E226376 entity
Predicate borderingAdministrativeUnit P224 FINISHED
Object Xiaogan E41038 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: Xiaogan | Statement: [Xiaochang County, borderingAdministrativeUnit, Xiaogan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xiaogan
Context triple: [Xiaochang County, borderingAdministrativeUnit, Xiaogan]
  • A. Xiaogan chosen
    Xiaogan is a prefecture-level city in central China known for its cultural heritage and proximity to the provincial capital, Wuhan, within Hubei Province.
  • B. 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.
  • 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. 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.
  • E. Xianning
    Xianning is a prefecture-level city in southeastern Hubei Province, China, known for its hot springs, karst landscapes, and historical sites.
  • 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_69ca843170f88190800a8ab2b5fc568e completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd51541020819097da2eb60be73760 completed April 1, 2026, 5:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69d74fa545b8819092809c605542476a completed April 9, 2026, 7:05 a.m.
Created at: March 30, 2026, 7:46 p.m.