Triple

T1629370
Position Surface form Disambiguated ID Type / Status
Subject Lingnan E35222 entity
Predicate language P15 FINISHED
Object Pinghua
Pinghua is a Sinitic language variety spoken primarily in parts of Guangxi and neighboring regions in southern China, often considered distinct from both Cantonese and Mandarin.
 E185259 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: Pinghua | Statement: [Lingnan, language, Pinghua]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Pinghua
Context triple: [Lingnan, language, Pinghua]
  • A. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • B. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • C. Paihuano
    Paihuano is a small town and commune in Chile’s Elqui Valley, known for its clear skies, pisco production, and astrotourism.
  • D. Kaihui
    Kaihui is a Chinese given name most notably borne by Yang Kaihui, the revolutionary and early partner of Mao Zedong.
  • E. Lingang
    Lingang is a rapidly developing industrial and high-tech district in Shanghai, China, known for hosting major manufacturing facilities such as Tesla’s Gigafactory Shanghai.
  • 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: Pinghua
Triple: [Lingnan, language, Pinghua]
Generated description
Pinghua is a Sinitic language variety spoken primarily in parts of Guangxi and neighboring regions in southern China, often considered distinct from both Cantonese and Mandarin.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Pinghua
Target entity description: Pinghua is a Sinitic language variety spoken primarily in parts of Guangxi and neighboring regions in southern China, often considered distinct from both Cantonese and Mandarin.
  • A. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • B. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • C. Paihuano
    Paihuano is a small town and commune in Chile’s Elqui Valley, known for its clear skies, pisco production, and astrotourism.
  • D. Kaihui
    Kaihui is a Chinese given name most notably borne by Yang Kaihui, the revolutionary and early partner of Mao Zedong.
  • E. Lingang
    Lingang is a rapidly developing industrial and high-tech district in Shanghai, China, known for hosting major manufacturing facilities such as Tesla’s Gigafactory Shanghai.
  • 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_69a886036bc081909ff5de16dbe5e8ea completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a909f257948190b3398fd6dc91f586 completed March 5, 2026, 4:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad58d5acd8819090c51678ce0f63f0 completed March 8, 2026, 11:09 a.m.
NEDg Description generation batch_69ad5a619da481908d66837ea94c91cf completed March 8, 2026, 11:15 a.m.
NED2 Entity disambiguation (via description) batch_69ad5b41a68c8190ba293d8e8c35521b completed March 8, 2026, 11:19 a.m.
Created at: March 4, 2026, 7:28 p.m.