Triple
T20387098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cho |
E497986
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Cho Yong-hyung
Cho Yong-hyung is a South Korean former professional footballer known for his role as a central defender and his appearances with the South Korean national team, including at the FIFA World Cup.
|
E1433457
|
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: Cho Yong-hyung | Statement: [Cho, hasNotableBearer, Cho Yong-hyung]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cho Yong-hyung Context triple: [Cho, hasNotableBearer, Cho Yong-hyung]
-
A.
Kim Tae-hyung
Kim Tae-hyung is a South Korean film director best known for his work in the horror and thriller genres.
-
B.
Cho Kyuhyun
Cho Kyuhyun is a South Korean singer, musical theatre actor, and television personality best known as a main vocalist of the K-pop boy group Super Junior.
-
C.
Jeonghan
Jeonghan is a South Korean singer best known as a vocalist and visual member of the K-pop boy group Seventeen.
-
D.
Song Il-kook
Song Il-kook is a South Korean actor best known internationally for his leading roles in historical television dramas such as "Jumong."
-
E.
Kim Haeyong
Kim Haeyong is a cinematographer best known for his work on the animated feature film "The Lego Ninjago Movie."
- 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: Cho Yong-hyung Triple: [Cho, hasNotableBearer, Cho Yong-hyung]
Generated description
Cho Yong-hyung is a South Korean former professional footballer known for his role as a central defender and his appearances with the South Korean national team, including at the FIFA World Cup.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cho Yong-hyung Target entity description: Cho Yong-hyung is a South Korean former professional footballer known for his role as a central defender and his appearances with the South Korean national team, including at the FIFA World Cup.
-
A.
Kim Tae-hyung
Kim Tae-hyung is a South Korean film director best known for his work in the horror and thriller genres.
-
B.
Cho Kyuhyun
Cho Kyuhyun is a South Korean singer, musical theatre actor, and television personality best known as a main vocalist of the K-pop boy group Super Junior.
-
C.
Jeonghan
Jeonghan is a South Korean singer best known as a vocalist and visual member of the K-pop boy group Seventeen.
-
D.
Song Il-kook
Song Il-kook is a South Korean actor best known internationally for his leading roles in historical television dramas such as "Jumong."
-
E.
Kim Haeyong
Kim Haeyong is a cinematographer best known for his work on the animated feature film "The Lego Ninjago Movie."
- 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_69e0b4a71ebc8190b153a36c738730f4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6790c935881908f901d058e6a83a9 |
completed | April 20, 2026, 7:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a088b0181a4819091030ed92f04d2dc |
completed | May 16, 2026, 3:19 p.m. |
| NEDg | Description generation | batch_6a088f2c8ccc8190a1dc02db8e799355 |
completed | May 16, 2026, 3:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a088f81fd0c8190a006db7e4fa9b440 |
completed | May 16, 2026, 3:38 p.m. |
Created at: April 16, 2026, 11:28 a.m.