Triple

T12127459
Position Surface form Disambiguated ID Type / Status
Subject Korea University E288843 entity
Predicate nickname P55 FINISHED
Object KU
KU is a leading private research university in Seoul, South Korea, renowned for its strong academic programs, vibrant student culture, and status as one of the country’s top institutions of higher education.
 E288843 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: KU | Statement: [Korea University, nickname, KU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KU
Context triple: [Korea University, nickname, KU]
  • A. KU
    KU is a common abbreviation for Kyoto University, a prestigious national research university in Kyoto, Japan.
  • B. KU
    KU is a common abbreviation for the University of Karachi, a major public research university in Karachi, Pakistan.
  • C. KU
    KU is the commonly used abbreviation for Kutztown University of Pennsylvania, a public university located in Kutztown, Pennsylvania.
  • D. KU
    KU is the commonly used abbreviation for Kettering University, a private university in Flint, Michigan known for its strong engineering and cooperative education programs.
  • E. KU
    KU is the University of Kansas, a major public research university in Lawrence, Kansas, known for its strong athletics and distinctive school traditions.
  • 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: KU
Triple: [Korea University, nickname, KU]
Generated description
KU is a leading private research university in Seoul, South Korea, renowned for its strong academic programs, vibrant student culture, and status as one of the country’s top institutions of higher education.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KU
Target entity description: KU is a leading private research university in Seoul, South Korea, renowned for its strong academic programs, vibrant student culture, and status as one of the country’s top institutions of higher education.
  • A. KU chosen
    KU is the commonly used abbreviation for Korea University, one of South Korea’s leading private research universities.
  • B. KU
    KU is the University of Kansas, a major public research university in Lawrence, Kansas, known for its strong athletics and distinctive school traditions.
  • C. KU
    KU is a common abbreviation for Kyoto University, a prestigious national research university in Kyoto, Japan.
  • D. KU
    KU is a common abbreviation for the University of Karachi, a major public research university in Karachi, Pakistan.
  • E. KU
    KU is the commonly used abbreviation for Kettering University, a private university in Flint, Michigan known for its strong engineering and cooperative education programs.
  • F. None of above.

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_69d6ab4b5e4c81909950b17151eb0951 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9157ce6b88190b16592cc48244db3 completed April 10, 2026, 3:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f688ad2481909d11782c44b3217f completed May 2, 2026, 1:05 p.m.
NEDg Description generation batch_69f5fdebe3fc81909a5bb23a943c3c43 completed May 2, 2026, 1:36 p.m.
NED2 Entity disambiguation (via description) batch_69f5feeaf2e48190995f282b02a9caaf completed May 2, 2026, 1:40 p.m.
Created at: April 8, 2026, 9:49 p.m.