Triple

T3955061
Position Surface form Disambiguated ID Type / Status
Subject Huizhou (historical region) E84955 entity
Predicate hasCity P316 FINISHED
Object Yixian
Yixian is a historic county-level city in China’s Anhui province, noted for its well-preserved ancient villages and traditional Huizhou architecture.
 E402898 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: Yixian | Statement: [Huizhou (historical region), hasCity, Yixian]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Yixian
Context triple: [Huizhou (historical region), hasCity, Yixian]
  • A. Xingzhen
    Xingzhen was the personal given name of Empress Dowager Cixi, the powerful de facto ruler of the late Qing dynasty in China.
  • B. Yuxiang
    Yuxiang is a Chinese given name notably borne by the early 20th-century warlord and military leader Feng Yuxiang.
  • C. Yuanhong
    Yuanhong is a Chinese given name that appears in the full name of the historical figure Li Yuanhong.
  • D. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • E. Xiaochang
    Xiaochang is a county in Hubei Province, China, known historically as a rural mission and teaching post where figures like Eric Liddell worked.
  • 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: Yixian
Triple: [Huizhou (historical region), hasCity, Yixian]
Generated description
Yixian is a historic county-level city in China’s Anhui province, noted for its well-preserved ancient villages and traditional Huizhou architecture.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Yixian
Target entity description: Yixian is a historic county-level city in China’s Anhui province, noted for its well-preserved ancient villages and traditional Huizhou architecture.
  • A. Xingzhen
    Xingzhen was the personal given name of Empress Dowager Cixi, the powerful de facto ruler of the late Qing dynasty in China.
  • B. Yuxiang
    Yuxiang is a Chinese given name notably borne by the early 20th-century warlord and military leader Feng Yuxiang.
  • C. Yuanhong
    Yuanhong is a Chinese given name that appears in the full name of the historical figure Li Yuanhong.
  • D. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • E. Xiaochang
    Xiaochang is a county in Hubei Province, China, known historically as a rural mission and teaching post where figures like Eric Liddell worked.
  • 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_69aed934fbfc8190847068e4546de963 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aef93d742c81908639c843193d78fd completed March 9, 2026, 4:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69b533aea08c8190b83d83e3ba89848c completed March 14, 2026, 10:08 a.m.
NEDg Description generation batch_69b537f7e2e481909b7a337c130bca7a completed March 14, 2026, 10:27 a.m.
NED2 Entity disambiguation (via description) batch_69b538a7f8e4819087a74e96255e7c45 completed March 14, 2026, 10:30 a.m.
Created at: March 9, 2026, 3:30 p.m.