Triple
T11772277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yonsei–Korea sports rivalry |
E279928
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
Ko-Yeon Jeon
Ko-Yeon Jeon is the traditional and highly anticipated annual sports rivalry event between Yonsei University and Korea University in South Korea.
|
E945806
|
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: Ko-Yeon Jeon | Statement: [Yonsei–Korea sports rivalry, alsoKnownAs, Ko-Yeon Jeon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ko-Yeon Jeon Context triple: [Yonsei–Korea sports rivalry, alsoKnownAs, Ko-Yeon Jeon]
-
A.
Da-yeon Jung
Da-yeon Jung is a Korean individual notable enough to be recognized as a prominent bearer of the surname Jung.
-
B.
Ji-hyun Jung
Ji-hyun Jung is a Korean given name borne by various notable individuals, including figures in entertainment, sports, and other public fields.
-
C.
Hyein Park
Hyein Park is a Korean-Canadian voice actress best known for voicing the character Abby in Pixar’s animated film "Turning Red."
-
D.
Dongjin Seo
Dongjin Seo is a neuroscientist and engineer known as one of the co-founders of the brain–computer interface company Neuralink.
-
E.
Kinam Kim
Kinam Kim is a prominent South Korean semiconductor executive and technologist recognized for his leadership and contributions to the global chip industry.
- 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: Ko-Yeon Jeon Triple: [Yonsei–Korea sports rivalry, alsoKnownAs, Ko-Yeon Jeon]
Generated description
Ko-Yeon Jeon is the traditional and highly anticipated annual sports rivalry event between Yonsei University and Korea University in South Korea.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ko-Yeon Jeon Target entity description: Ko-Yeon Jeon is the traditional and highly anticipated annual sports rivalry event between Yonsei University and Korea University in South Korea.
-
A.
Da-yeon Jung
Da-yeon Jung is a Korean individual notable enough to be recognized as a prominent bearer of the surname Jung.
-
B.
Ji-hyun Jung
Ji-hyun Jung is a Korean given name borne by various notable individuals, including figures in entertainment, sports, and other public fields.
-
C.
Hyein Park
Hyein Park is a Korean-Canadian voice actress best known for voicing the character Abby in Pixar’s animated film "Turning Red."
-
D.
Dongjin Seo
Dongjin Seo is a neuroscientist and engineer known as one of the co-founders of the brain–computer interface company Neuralink.
-
E.
Kinam Kim
Kinam Kim is a prominent South Korean semiconductor executive and technologist recognized for his leadership and contributions to the global chip industry.
- 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_69d6ab01d2688190ad8ed6bda487eaa5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a55dfa088190a59b35d0247225e3 |
completed | April 10, 2026, 7:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f09086c4ec81908bc8b707a49c3ac2 |
completed | April 28, 2026, 10:48 a.m. |
| NEDg | Description generation | batch_69f0bd3cf8308190813003daa8cfba4a |
completed | April 28, 2026, 1:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f0ef02c930819086d139834ad4ed84 |
completed | April 28, 2026, 5:31 p.m. |
Created at: April 8, 2026, 9:41 p.m.