Triple

T8705033
Position Surface form Disambiguated ID Type / Status
Subject Xiaogan E206626 entity
Predicate pinyinName P9333 FINISHED
Object Xiàogǎn E206626 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: Xiàogǎn | Statement: [Xiaogan, pinyinName, Xiàogǎn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xiàogǎn
Context triple: [Xiaogan, pinyinName, Xiàogǎn]
  • A. Xiàogǎn chosen
    Xiàogǎn is a prefecture-level city in central China’s Hubei province, known for its historical significance and proximity to Wuhan.
  • B. Xiaoerjing
    Xiaoerjing is an Arabic-based writing system historically used to transcribe Sinitic languages, especially by Muslim communities in China such as the Hui.
  • C. Xiao Ke
    Xiao Ke was a prominent Chinese military leader and general of the People’s Liberation Army who played key roles in the Chinese Civil War and early PRC military development.
  • D. Xiaobo
    Xiaobo is the given name of Liu Xiaobo, the Chinese literary critic, human rights activist, and Nobel Peace Prize laureate.
  • E. Xiaozong
    Xiaozong is the temple name of the Hongzhi Emperor, a Ming dynasty ruler noted for his relatively peaceful and reform-minded reign in late 15th-century China.
  • 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_69ca835645e881908f00e3c8b51da81d completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc58fb43f081909df5d1e31cb1ec04 completed March 31, 2026, 11:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69cef423b1c4819081ea887af8f8e576 completed April 2, 2026, 10:56 p.m.
Created at: March 30, 2026, 6:34 p.m.