Triple

T2395922
Position Surface form Disambiguated ID Type / Status
Subject Yang Hucheng E47650 entity
Predicate givenName P17 FINISHED
Object Hucheng
Hucheng is the given name of Yang Hucheng, a prominent Chinese general and political figure best known for his role in the Xi'an Incident of 1936.
 E286524 NE FINISHED

How this triple was built (4 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: Hucheng | Statement: [Yang Hucheng, givenName, Hucheng]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hucheng
Context triple: [Yang Hucheng, givenName, Hucheng]
  • A. Hanchuan
    Hanchuan is a county-level city in central China's Hubei Province, known for its location within the fertile Jianghan Plain and its role in regional agriculture and industry.
  • B. Jianye
    Jianye is an ancient name for the city now known as Nanjing, a historically significant capital in several Chinese dynasties.
  • C. Yulin
    Yulin is a prefecture-level city in northern China known for its coal resources and location on the Loess Plateau near the border with Inner Mongolia.
  • D. Xinzhuang
    Xinzhuang is a major suburban town and transportation hub in Shanghai, China, known for its busy commercial areas and key metro and rail connections.
  • E. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Hucheng
Triple: [Yang Hucheng, givenName, Hucheng]
Generated description
Hucheng is the given name of Yang Hucheng, a prominent Chinese general and political figure best known for his role in the Xi'an Incident of 1936.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hucheng
Target entity description: Hucheng is the given name of Yang Hucheng, a prominent Chinese general and political figure best known for his role in the Xi'an Incident of 1936.
  • A. Hanchuan
    Hanchuan is a county-level city in central China's Hubei Province, known for its location within the fertile Jianghan Plain and its role in regional agriculture and industry.
  • B. Jianye
    Jianye is an ancient name for the city now known as Nanjing, a historically significant capital in several Chinese dynasties.
  • C. Yulin
    Yulin is a prefecture-level city in northern China known for its coal resources and location on the Loess Plateau near the border with Inner Mongolia.
  • D. Xinzhuang
    Xinzhuang is a major suburban town and transportation hub in Shanghai, China, known for its busy commercial areas and key metro and rail connections.
  • E. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • F. None of above. chosen

Provenance (5 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_69a88a1c450c81909f61abb8b6863885 completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abc879b1b88190be8d0337d9a17bd0 completed March 7, 2026, 6:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69af98a342508190b30766327f298bf9 completed March 10, 2026, 4:05 a.m.
NEDg Description generation batch_69af99d25a1c81908061ebf996e4f470 completed March 10, 2026, 4:10 a.m.
NED2 Entity disambiguation (via description) batch_69af9a94473c8190b97cedd374d9b032 completed March 10, 2026, 4:14 a.m.
Created at: March 4, 2026, 7:57 p.m.