Triple
T20387096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cho |
E497986
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Cho Jae-wan
Cho Jae-wan is a Korean individual notable enough to be specifically cited as a bearer of the Korean surname Cho.
|
E1545692
|
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 Jae-wan | Statement: [Cho, hasNotableBearer, Cho Jae-wan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cho Jae-wan Context triple: [Cho, hasNotableBearer, Cho Jae-wan]
-
A.
Jin Kyeong-hun
Jin Kyeong-hun is a central character in the South Korean dark fantasy series "Hellbound," depicted as a determined detective entangled in the mysterious and terrifying supernatural decrees that suddenly begin condemning people to hell.
-
B.
Shin Dong-ik
Shin Dong-ik is a South Korean screenwriter known for his work on popular films such as the fantasy-comedy "Miss Granny."
-
C.
Kim Hong-gul
Kim Hong-gul is a South Korean politician and the son of former President and Nobel Peace Prize laureate Kim Dae-jung.
-
D.
Kim Dong-wook
Kim Dong-wook is a composer known for creating the musical score for the South Korean dark fantasy series "Hellbound."
-
E.
Kim Sang-bum
Kim Sang-bum is a South Korean film editor known for his work on numerous acclaimed Korean movies.
- 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 Jae-wan Triple: [Cho, hasNotableBearer, Cho Jae-wan]
Generated description
Cho Jae-wan is a Korean individual notable enough to be specifically cited as a bearer of the Korean surname Cho.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cho Jae-wan Target entity description: Cho Jae-wan is a Korean individual notable enough to be specifically cited as a bearer of the Korean surname Cho.
-
A.
Jin Kyeong-hun
Jin Kyeong-hun is a central character in the South Korean dark fantasy series "Hellbound," depicted as a determined detective entangled in the mysterious and terrifying supernatural decrees that suddenly begin condemning people to hell.
-
B.
Shin Dong-ik
Shin Dong-ik is a South Korean screenwriter known for his work on popular films such as the fantasy-comedy "Miss Granny."
-
C.
Kim Hong-gul
Kim Hong-gul is a South Korean politician and the son of former President and Nobel Peace Prize laureate Kim Dae-jung.
-
D.
Kim Dong-wook
Kim Dong-wook is a composer known for creating the musical score for the South Korean dark fantasy series "Hellbound."
-
E.
Kim Sang-bum
Kim Sang-bum is a South Korean film editor known for his work on numerous acclaimed Korean movies.
- 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_6a0b4de0dec48190a74ccdef29499dfb |
completed | May 18, 2026, 5:35 p.m. |
| NEDg | Description generation | batch_6a0b4f6389988190ad3772c081d74dac |
completed | May 18, 2026, 5:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b500dd6d08190876c8ea69f53dec0 |
completed | May 18, 2026, 5:44 p.m. |
Created at: April 16, 2026, 11:28 a.m.