Triple

T5690542
Position Surface form Disambiguated ID Type / Status
Subject Middle Chinese E125417 entity
Predicate primarySource P2296 FINISHED
Object Guangyun
Guangyun is an 11th-century Chinese rime dictionary that serves as a major source for reconstructing the phonology of Middle Chinese.
 E541872 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: Guangyun | Statement: [Middle Chinese, primarySource, Guangyun]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Guangyun
Context triple: [Middle Chinese, primarySource, Guangyun]
  • A. Guangyi
    Guangyi was a warship that served in China's late 19th-century Beiyang Fleet, one of the Qing dynasty's principal modern naval forces.
  • B. Xuan
    Xuan is a Vietnamese surname commonly used as a family name in Vietnam.
  • 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. Guangjia
    Guangjia was a warship of China’s late 19th-century Beiyang Fleet, one of the Qing dynasty’s principal modern naval forces.
  • E. Yuanhong
    Yuanhong is a Chinese given name that appears in the full name of the historical figure Li Yuanhong.
  • 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: Guangyun
Triple: [Middle Chinese, primarySource, Guangyun]
Generated description
Guangyun is an 11th-century Chinese rime dictionary that serves as a major source for reconstructing the phonology of Middle Chinese.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Guangyun
Target entity description: Guangyun is an 11th-century Chinese rime dictionary that serves as a major source for reconstructing the phonology of Middle Chinese.
  • A. Guangyi
    Guangyi was a warship that served in China's late 19th-century Beiyang Fleet, one of the Qing dynasty's principal modern naval forces.
  • B. Xuan
    Xuan is a Vietnamese surname commonly used as a family name in Vietnam.
  • 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. Guangjia
    Guangjia was a warship of China’s late 19th-century Beiyang Fleet, one of the Qing dynasty’s principal modern naval forces.
  • E. Yuanhong
    Yuanhong is a Chinese given name that appears in the full name of the historical figure Li Yuanhong.
  • 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_69c0082bb19c8190823a4facd3cba79b completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c023e340a08190b6175fad3e9a32b6 completed March 22, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a47457c8190bc75f11a7f011a8a completed March 22, 2026, 9:08 p.m.
NEDg Description generation batch_69c05dc222e88190a1d715392ae24f08 completed March 22, 2026, 9:23 p.m.
NED2 Entity disambiguation (via description) batch_69c0620ee1848190935f5f78abbed7ba completed March 22, 2026, 9:41 p.m.
Created at: March 22, 2026, 3:44 p.m.