Triple

T5755441
Position Surface form Disambiguated ID Type / Status
Subject Zhuang language E126954 entity
Predicate hasDialects P4251 FINISHED
Object Wuming Zhuang
Wuming Zhuang is a major variety of the Zhuang language spoken primarily in the Wuming District of Guangxi, China.
 E545686 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: Wuming Zhuang | Statement: [Zhuang language, hasDialects, Wuming Zhuang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wuming Zhuang
Context triple: [Zhuang language, hasDialects, Wuming Zhuang]
  • A. Fangzhuang
    Fangzhuang is a residential neighborhood and commercial area in Beijing, China, known as one of the city’s earlier large-scale planned communities.
  • B. Muzong
    Muzong is the temple name of the Longqing Emperor, a Ming dynasty ruler of China who reigned from 1567 to 1572.
  • C. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • D. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • E. 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.
  • 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: Wuming Zhuang
Triple: [Zhuang language, hasDialects, Wuming Zhuang]
Generated description
Wuming Zhuang is a major variety of the Zhuang language spoken primarily in the Wuming District of Guangxi, China.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wuming Zhuang
Target entity description: Wuming Zhuang is a major variety of the Zhuang language spoken primarily in the Wuming District of Guangxi, China.
  • A. Fangzhuang
    Fangzhuang is a residential neighborhood and commercial area in Beijing, China, known as one of the city’s earlier large-scale planned communities.
  • B. Muzong
    Muzong is the temple name of the Longqing Emperor, a Ming dynasty ruler of China who reigned from 1567 to 1572.
  • C. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • D. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • E. 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.
  • 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_69c00832aedc81909899801b141fa3b4 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c02906848c8190bf7b0d62f57c27fa completed March 22, 2026, 5:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07e47c1788190b5883df385475237 completed March 22, 2026, 11:41 p.m.
NEDg Description generation batch_69c08e5a7950819099cd9c9bd6c7a99a completed March 23, 2026, 12:50 a.m.
NED2 Entity disambiguation (via description) batch_69c08ed0c3ac8190a7093667bcd4fb0f completed March 23, 2026, 12:52 a.m.
Created at: March 22, 2026, 3:49 p.m.