Triple

T3796389
Position Surface form Disambiguated ID Type / Status
Subject Daxing District E89779 entity
Predicate hasCapital P204 FINISHED
Object Huangcun
Huangcun is a town in Beijing, China, that serves as the administrative and commercial center of the city's southern Daxing District.
 E391596 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: Huangcun | Statement: [Daxing District, hasCapital, Huangcun]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Huangcun
Context triple: [Daxing District, hasCapital, Huangcun]
  • A. Ruchang
    Ruchang is a Chinese given name most notably borne by Ding Ruchang, a late Qing dynasty naval commander.
  • B. Longqing
    Longqing was the era name of a brief but notable period of the Ming dynasty in China, associated with the reign of the Longqing Emperor in the 16th century.
  • C. 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.
  • D. 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.
  • E. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • 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: Huangcun
Triple: [Daxing District, hasCapital, Huangcun]
Generated description
Huangcun is a town in Beijing, China, that serves as the administrative and commercial center of the city's southern Daxing District.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Huangcun
Target entity description: Huangcun is a town in Beijing, China, that serves as the administrative and commercial center of the city's southern Daxing District.
  • A. Ruchang
    Ruchang is a Chinese given name most notably borne by Ding Ruchang, a late Qing dynasty naval commander.
  • B. Longqing
    Longqing was the era name of a brief but notable period of the Ming dynasty in China, associated with the reign of the Longqing Emperor in the 16th century.
  • C. 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.
  • D. 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.
  • E. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • 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_69aed9597d6881909b6ee3b9de859223 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aee79f09bc8190b7514a11a030eba5 completed March 9, 2026, 3:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4fb232650819089feed9d5bb8c91a completed March 14, 2026, 6:07 a.m.
NEDg Description generation batch_69b4fc7abfb481908f563e17e6e57a4e completed March 14, 2026, 6:13 a.m.
NED2 Entity disambiguation (via description) batch_69b4fce0a6a88190be19941a70caebdc completed March 14, 2026, 6:14 a.m.
Created at: March 9, 2026, 3:15 p.m.