Triple

T7888338
Position Surface form Disambiguated ID Type / Status
Subject Kim Dae-jung E183159 entity
Predicate successor P78 FINISHED
Object Roh Moo-hyun
Roh Moo-hyun was a South Korean human rights lawyer-turned-politician who served as the country’s president from 2003 to 2008, known for his reformist agenda and efforts to engage North Korea.
 E702208 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: Roh Moo-hyun | Statement: [Kim Dae-jung, successor, Roh Moo-hyun]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Roh Moo-hyun
Context triple: [Kim Dae-jung, successor, Roh Moo-hyun]
  • A. Lee Myung-bak
    Lee Myung-bak is a South Korean businessman-turned-politician who served as the President of South Korea from 2008 to 2013.
  • B. 김대중
    김대중은 대한민국의 제15대 대통령으로, 민주화 운동과 남북 화해 정책(햇볕정책)을 주도해 노벨 평화상을 수상한 정치인이다.
  • C. Kim Young-sam
    Kim Young-sam was a South Korean politician who served as the country’s president in the 1990s and is known for advancing democratic reforms and anti-corruption measures.
  • D. John Kim
    John Kim is an Australian actor best known for his role as Ezekiel Jones in the fantasy-adventure television series "The Librarians."
  • E. John Kim
    John Kim is a prominent mechanical engineer and researcher renowned for his pioneering work in computational fluid dynamics and turbulence modeling.
  • 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: Roh Moo-hyun
Triple: [Kim Dae-jung, successor, Roh Moo-hyun]
Generated description
Roh Moo-hyun was a South Korean human rights lawyer-turned-politician who served as the country’s president from 2003 to 2008, known for his reformist agenda and efforts to engage North Korea.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Roh Moo-hyun
Target entity description: Roh Moo-hyun was a South Korean human rights lawyer-turned-politician who served as the country’s president from 2003 to 2008, known for his reformist agenda and efforts to engage North Korea.
  • A. Lee Myung-bak
    Lee Myung-bak is a South Korean businessman-turned-politician who served as the President of South Korea from 2008 to 2013.
  • B. 김대중
    김대중은 대한민국의 제15대 대통령으로, 민주화 운동과 남북 화해 정책(햇볕정책)을 주도해 노벨 평화상을 수상한 정치인이다.
  • C. Kim Young-sam
    Kim Young-sam was a South Korean politician who served as the country’s president in the 1990s and is known for advancing democratic reforms and anti-corruption measures.
  • D. John Kim
    John Kim is an Australian actor best known for his role as Ezekiel Jones in the fantasy-adventure television series "The Librarians."
  • E. John Kim
    John Kim is a prominent mechanical engineer and researcher renowned for his pioneering work in computational fluid dynamics and turbulence modeling.
  • 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_69ca828af6e48190a06ee7010d8f0e64 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb39ea8d1c81908ef99569e0cf00b7 completed March 31, 2026, 3:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69cbdfb5d7e08190a04dc9d3dc35a0e6 completed March 31, 2026, 2:52 p.m.
NEDg Description generation batch_69cbe4888800819080eb11c9b7b7e28f completed March 31, 2026, 3:13 p.m.
NED2 Entity disambiguation (via description) batch_69cc1175c8a481909b29d3b850ad9084 completed March 31, 2026, 6:24 p.m.
Created at: March 30, 2026, 4:59 p.m.