Triple

T11902771
Position Surface form Disambiguated ID Type / Status
Subject Peng Yuchang E283195 entity
Predicate givenName P17 FINISHED
Object Yuchang
Yuchang is a Chinese actor and singer best known for his roles in popular youth and coming-of-age films and television dramas.
 E954345 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: Yuchang | Statement: [Peng Yuchang, givenName, Yuchang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Yuchang
Context triple: [Peng Yuchang, givenName, Yuchang]
  • A. Yangsan
    Yangsan is a city in South Gyeongsang Province, South Korea, known as a growing residential and educational hub near Busan.
  • B. Yuncheng
    Yuncheng is a major city in southern Shanxi Province, China, known for its historical sites and role as a regional transportation and economic hub.
  • C. Hejin
    Hejin is a county-level city in southern Shanxi Province, China, situated along the Fen River near its confluence with the Yellow River.
  • D. Songyuan
    Songyuan is a prefecture-level city in northwestern Jilin Province, China, known as an important regional hub for agriculture, petrochemicals, and transportation.
  • E. Changshou
    Changshou was a Chinese imperial era name used during the reign of Empress Wu Zetian in the Tang dynasty.
  • 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: Yuchang
Triple: [Peng Yuchang, givenName, Yuchang]
Generated description
Yuchang is a Chinese actor and singer best known for his roles in popular youth and coming-of-age films and television dramas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Yuchang
Target entity description: Yuchang is a Chinese actor and singer best known for his roles in popular youth and coming-of-age films and television dramas.
  • A. Yangsan
    Yangsan is a city in South Gyeongsang Province, South Korea, known as a growing residential and educational hub near Busan.
  • B. Yuncheng
    Yuncheng is a major city in southern Shanxi Province, China, known for its historical sites and role as a regional transportation and economic hub.
  • C. Hejin
    Hejin is a county-level city in southern Shanxi Province, China, situated along the Fen River near its confluence with the Yellow River.
  • D. Songyuan
    Songyuan is a prefecture-level city in northwestern Jilin Province, China, known as an important regional hub for agriculture, petrochemicals, and transportation.
  • E. Changshou
    Changshou was a Chinese imperial era name used during the reign of Empress Wu Zetian in the Tang dynasty.
  • 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_69d6ab2c07e88190ba13b0d21fd6cf33 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8dd1792648190853f15fbf217eebd completed April 10, 2026, 11:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69f44004b454819091b41bac99895106 completed May 1, 2026, 5:54 a.m.
NEDg Description generation batch_69f448fa8eec81909fe6ac0902f46998 completed May 1, 2026, 6:32 a.m.
NED2 Entity disambiguation (via description) batch_69f44aef15148190ba8090681b921ffa completed May 1, 2026, 6:40 a.m.
Created at: April 8, 2026, 9:44 p.m.