Triple

T2639799
Position Surface form Disambiguated ID Type / Status
Subject Ding Ruchang E62836 entity
Predicate givenName P17 FINISHED
Object Ruchang
Ruchang is a Chinese given name most notably borne by Ding Ruchang, a late Qing dynasty naval commander.
 E289090 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: Ruchang | Statement: [Ding Ruchang, givenName, Ruchang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ruchang
Context triple: [Ding Ruchang, givenName, Ruchang]
  • A. 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.
  • B. Jianye
    Jianye is an ancient name for the city now known as Nanjing, a historically significant capital in several Chinese dynasties.
  • C. 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.
  • D. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • E. Changling
    Changling is the largest and best-preserved mausoleum within Beijing’s Ming Tombs complex, built for the Yongle Emperor and his empress.
  • 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: Ruchang
Triple: [Ding Ruchang, givenName, Ruchang]
Generated description
Ruchang is a Chinese given name most notably borne by Ding Ruchang, a late Qing dynasty naval commander.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ruchang
Target entity description: Ruchang is a Chinese given name most notably borne by Ding Ruchang, a late Qing dynasty naval commander.
  • A. 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.
  • B. Jianye
    Jianye is an ancient name for the city now known as Nanjing, a historically significant capital in several Chinese dynasties.
  • C. 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.
  • D. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • E. Changling
    Changling is the largest and best-preserved mausoleum within Beijing’s Ming Tombs complex, built for the Yongle Emperor and his empress.
  • 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_69ab4c3f2dcc819082df80f5e032f690 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abd8fafad08190939b08558fea6abd completed March 7, 2026, 7:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69afa04f0d448190adf113831fb5bc42 completed March 10, 2026, 4:38 a.m.
NEDg Description generation batch_69afa12b8d388190a50de5f41c0fa782 completed March 10, 2026, 4:42 a.m.
NED2 Entity disambiguation (via description) batch_69afa4e2f56c8190a418aef660da8e12 completed March 10, 2026, 4:58 a.m.
Created at: March 6, 2026, 9:53 p.m.