Triple
T20387087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cho |
E497986
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Cho Hae-jung
Cho Hae-jung is a South Korean actress known for her roles in television dramas and films.
|
E1468777
|
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 Hae-jung | Statement: [Cho, hasNotableBearer, Cho Hae-jung]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cho Hae-jung Context triple: [Cho, hasNotableBearer, Cho Hae-jung]
-
A.
Jo Sung-hee
Jo Sung-hee is a South Korean film director and screenwriter known for his work in genre-blending films and large-scale science fiction cinema.
-
B.
Lee Soon-ja
Lee Soon-ja is the widow of former South Korean president and military ruler Chun Doo-hwan and a prominent, often controversial, figure in South Korea's modern political history.
-
C.
Cha Jeong-in
Cha Jeong-in is a South Korean academic who serves as the president of Pusan National University.
-
D.
Son Ki-jung
Son Ki-jung was a Korean marathon runner who won the gold medal at the 1936 Berlin Olympics while competing for Japan under the name Son Kee-chung.
-
E.
Kim Hyun-sook
Kim Hyun-sook is a South Korean actress known for her comedic and character roles in film, television, and theater.
- 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 Hae-jung Triple: [Cho, hasNotableBearer, Cho Hae-jung]
Generated description
Cho Hae-jung is a South Korean actress known for her roles in television dramas and films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cho Hae-jung Target entity description: Cho Hae-jung is a South Korean actress known for her roles in television dramas and films.
-
A.
Jo Sung-hee
Jo Sung-hee is a South Korean film director and screenwriter known for his work in genre-blending films and large-scale science fiction cinema.
-
B.
Lee Soon-ja
Lee Soon-ja is the widow of former South Korean president and military ruler Chun Doo-hwan and a prominent, often controversial, figure in South Korea's modern political history.
-
C.
Cha Jeong-in
Cha Jeong-in is a South Korean academic who serves as the president of Pusan National University.
-
D.
Son Ki-jung
Son Ki-jung was a Korean marathon runner who won the gold medal at the 1936 Berlin Olympics while competing for Japan under the name Son Kee-chung.
-
E.
Kim Hyun-sook
Kim Hyun-sook is a South Korean actress known for her comedic and character roles in film, television, and theater.
- 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_6a096da69304819083401bfe8244b594 |
completed | May 17, 2026, 7:26 a.m. |
| NEDg | Description generation | batch_6a096ec6259c8190a01b2079c50fddb2 |
completed | May 17, 2026, 7:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a096f837ac0819094aa4bd6cf59a1e2 |
completed | May 17, 2026, 7:34 a.m. |
Created at: April 16, 2026, 11:28 a.m.