Triple

T15278341
Position Surface form Disambiguated ID Type / Status
Subject Xu Shichang E365200 entity
Predicate givenName P17 FINISHED
Object Shichang
Shichang is the given name of Xu Shichang, a prominent early 20th-century Chinese politician who served as President of the Republic of China.
E1154483 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: Shichang | Statement: [Xu Shichang, givenName, Shichang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shichang
Context triple: [Xu Shichang, givenName, Shichang]
  • A. Changshou
    Changshou was a Chinese imperial era name used during the reign of Empress Wu Zetian in the Tang dynasty.
  • B. Shancheng
    Shancheng is a Chinese nickname meaning "Mountain City," commonly used to refer to the city of Chongqing, known for its steep terrain and hilly urban landscape.
  • C. Yangsan
    Yangsan is a city in South Gyeongsang Province, South Korea, known as a growing residential and educational hub near Busan.
  • D. Yuchang
    Yuchang is a Chinese actor and singer best known for his roles in popular youth and coming-of-age films and television dramas.
  • E. Weida
    Weida is a river in eastern Germany that serves as a significant tributary of the Weiße Elster.
  • 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: Shichang
Triple: [Xu Shichang, givenName, Shichang]
Generated description
Shichang is the given name of Xu Shichang, a prominent early 20th-century Chinese politician who served as President of the Republic of China.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Shichang
Target entity description: Shichang is the given name of Xu Shichang, a prominent early 20th-century Chinese politician who served as President of the Republic of China.
  • A. Changshou
    Changshou was a Chinese imperial era name used during the reign of Empress Wu Zetian in the Tang dynasty.
  • B. Shancheng
    Shancheng is a Chinese nickname meaning "Mountain City," commonly used to refer to the city of Chongqing, known for its steep terrain and hilly urban landscape.
  • C. Yangsan
    Yangsan is a city in South Gyeongsang Province, South Korea, known as a growing residential and educational hub near Busan.
  • D. Yuchang
    Yuchang is a Chinese actor and singer best known for his roles in popular youth and coming-of-age films and television dramas.
  • E. Weida
    Weida is a river in eastern Germany that serves as a significant tributary of the Weiße Elster.
  • 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_69d85a103d9081908c1ea6c4c73ac8e3 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00953bc848190b83919f39d5ee37b completed April 15, 2026, 9:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff133b333c81908e38e9681bf81e40 completed May 9, 2026, 10:58 a.m.
NEDg Description generation batch_69ff142e99e081909d01cac0416f1bde completed May 9, 2026, 11:02 a.m.
NED2 Entity disambiguation (via description) batch_69ff14c61eb08190ba854b541eb1ce14 completed May 9, 2026, 11:04 a.m.
Created at: April 10, 2026, 3:14 a.m.