Triple

T410310
Position Surface form Disambiguated ID Type / Status
Subject Peng Chun Chang E9474 entity
Predicate givenName P17 FINISHED
Object Chun
Chun is the given name of Peng Chun Chang, a prominent Chinese philosopher and diplomat who helped draft the Universal Declaration of Human Rights.
 E51980 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: Chun | Statement: [Peng Chun Chang, givenName, Chun]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Chun
Context triple: [Peng Chun Chang, givenName, Chun]
  • A. Chan
    Chan is a common Chinese surname shared by many notable individuals across various fields worldwide.
  • B. Hana
    Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during World War II.
  • C. Shōhō
    Shōhō was a Japanese light aircraft carrier of the Imperial Japanese Navy during World War II, notable for being the first Japanese carrier sunk in the war during the Battle of the Coral Sea.
  • D. Xuan
    Xuan is a Vietnamese surname commonly used as a family name in Vietnam.
  • E. Hira
    Hira is the ISO 15924 script code representing the Japanese hiragana writing system.
  • 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: Chun
Triple: [Peng Chun Chang, givenName, Chun]
Generated description
Chun is the given name of Peng Chun Chang, a prominent Chinese philosopher and diplomat who helped draft the Universal Declaration of Human Rights.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Chun
Target entity description: Chun is the given name of Peng Chun Chang, a prominent Chinese philosopher and diplomat who helped draft the Universal Declaration of Human Rights.
  • A. Chan
    Chan is a common Chinese surname shared by many notable individuals across various fields worldwide.
  • B. Hana
    Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during World War II.
  • C. Shōhō
    Shōhō was a Japanese light aircraft carrier of the Imperial Japanese Navy during World War II, notable for being the first Japanese carrier sunk in the war during the Battle of the Coral Sea.
  • D. Xuan
    Xuan is a Vietnamese surname commonly used as a family name in Vietnam.
  • E. Hira
    Hira is the ISO 15924 script code representing the Japanese hiragana writing system.
  • 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_69a2e80111fc8190961d5b7c6154123f completed Feb. 28, 2026, 1:05 p.m.
NER Named-entity recognition batch_69a2ecd96fec8190948fe64928ab4d85 completed Feb. 28, 2026, 1:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4177a3ef88190bb331ab9034ead7d completed March 1, 2026, 10:39 a.m.
NEDg Description generation batch_69a4183d18348190b8518ae51d29a57c completed March 1, 2026, 10:43 a.m.
NED2 Entity disambiguation (via description) batch_69a41897ffe88190a8c166c8b07e0cbf completed March 1, 2026, 10:44 a.m.
Created at: Feb. 28, 2026, 1:09 p.m.