Triple

T8681638
Position Surface form Disambiguated ID Type / Status
Subject Feng E206051 entity
Predicate hasChineseCharacterForm P63661 FINISHED
Object 冯
冯 is a common Chinese surname with ancient origins, used by many notable historical and contemporary figures.
 E751169 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: 冯 | Statement: [Feng, hasChineseCharacterForm, 冯]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 冯
Context triple: [Feng, hasChineseCharacterForm, 冯]
  • A. 詹
    詹 is a Chinese surname and character commonly romanized as "Chan" in Cantonese.
  • B. 趙
    趙 is a common Chinese surname with historical roots in ancient China, notably associated with the State of Zhao during the Warring States period.
  • C. Fu Cong
    Fu Cong is a Chinese diplomat who serves as the Permanent Representative of the People’s Republic of China to the United Nations.
  • D. Fayan Wenyi
    Fayan Wenyi was a prominent 10th-century Chinese Chan (Zen) master and founder of the Fayan school, known for his influential teachings and role in the development of Song-dynasty Chan Buddhism.
  • E. Yunwen
    Yunwen was the personal name of the Jianwen Emperor, a Ming dynasty ruler of China known for his short and turbulent reign and subsequent mysterious disappearance.
  • 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: 冯
Triple: [Feng, hasChineseCharacterForm, 冯]
Generated description
冯 is a common Chinese surname with ancient origins, used by many notable historical and contemporary figures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 冯
Target entity description: 冯 is a common Chinese surname with ancient origins, used by many notable historical and contemporary figures.
  • A. 詹
    詹 is a Chinese surname and character commonly romanized as "Chan" in Cantonese.
  • B. 趙
    趙 is a common Chinese surname with historical roots in ancient China, notably associated with the State of Zhao during the Warring States period.
  • C. Fu Cong
    Fu Cong is a Chinese diplomat who serves as the Permanent Representative of the People’s Republic of China to the United Nations.
  • D. Fayan Wenyi
    Fayan Wenyi was a prominent 10th-century Chinese Chan (Zen) master and founder of the Fayan school, known for his influential teachings and role in the development of Song-dynasty Chan Buddhism.
  • E. Yunwen
    Yunwen was the personal name of the Jianwen Emperor, a Ming dynasty ruler of China known for his short and turbulent reign and subsequent mysterious disappearance.
  • 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_69ca835379688190aa06b9d98e684d58 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5c2e9c688190aceefaa2c3b7d7bd completed March 31, 2026, 11:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69cef3b9fb848190b7126f8f6a1ba76f completed April 2, 2026, 10:54 p.m.
NEDg Description generation batch_69cef521010081908815779c0bd2aac9 completed April 2, 2026, 11 p.m.
NED2 Entity disambiguation (via description) batch_69cef727ea088190bf40eaf5424ae864 completed April 2, 2026, 11:09 p.m.
Created at: March 30, 2026, 6:32 p.m.