Triple

T9918519
Position Surface form Disambiguated ID Type / Status
Subject Emperor Wu of Han E185930 entity
Predicate eraName P2938 FINISHED
Object Houyuan
Houyuan was an era name used during the reign of Emperor Wu of the Western Han dynasty in ancient China.
 E830510 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: Houyuan | Statement: [Emperor Wu of Han, eraName, Houyuan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Houyuan
Context triple: [Emperor Wu of Han, eraName, Houyuan]
  • A. Houyuan
    Houyuan was a historical Chinese era name used during the reign of Emperor Jing of the Western Han dynasty.
  • B. Zhaoyuan
    Zhaoyuan is a county-level city in eastern China's Shandong province, known for its rich gold mining industry and economic development.
  • C. Wuyuan
    Wuyuan is a historic county in northeastern Jiangxi, China, famed for its well-preserved Huizhou-style architecture and picturesque rural landscapes.
  • D. Yongcheng
    Yongcheng was a Qing dynasty imperial prince, known as one of the sons of the Qianlong Emperor of China.
  • E. Huangcun
    Huangcun is a town in Beijing, China, that serves as the administrative and commercial center of the city's southern Daxing District.
  • 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: Houyuan
Triple: [Emperor Wu of Han, eraName, Houyuan]
Generated description
Houyuan was an era name used during the reign of Emperor Wu of the Western Han dynasty in ancient China.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Houyuan
Target entity description: Houyuan was an era name used during the reign of Emperor Wu of the Western Han dynasty in ancient China.
  • A. Houyuan
    Houyuan was a historical Chinese era name used during the reign of Emperor Jing of the Western Han dynasty.
  • B. Zhaoyuan
    Zhaoyuan is a county-level city in eastern China's Shandong province, known for its rich gold mining industry and economic development.
  • C. Wuyuan
    Wuyuan is a historic county in northeastern Jiangxi, China, famed for its well-preserved Huizhou-style architecture and picturesque rural landscapes.
  • D. Yongcheng
    Yongcheng was a Qing dynasty imperial prince, known as one of the sons of the Qianlong Emperor of China.
  • E. Huangcun
    Huangcun is a town in Beijing, China, that serves as the administrative and commercial center of the city's southern Daxing District.
  • 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_69ca829b45f481909040f7b99a1976ed completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cdb5685a908190ab3e55b9bf9613f6 completed April 2, 2026, 12:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69d228b8974c81909a603407ebe3df1f completed April 5, 2026, 9:17 a.m.
NEDg Description generation batch_69d22990ef5881908b6a6100d7dcf6e6 completed April 5, 2026, 9:21 a.m.
NED2 Entity disambiguation (via description) batch_69d22a0cb0808190a6119dc0268c50b9 completed April 5, 2026, 9:23 a.m.
Created at: March 30, 2026, 8:42 p.m.