Triple
T22004087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sandglass |
E543404
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Kim Soo-mi
Kim Soo-mi is a veteran South Korean actress renowned for her prolific film and television career, particularly in comedy and family dramas.
|
E1572954
|
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: Kim Soo-mi | Statement: [Sandglass, hasCastMember, Kim Soo-mi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kim Soo-mi Context triple: [Sandglass, hasCastMember, Kim Soo-mi]
-
A.
Hong Yoon-jeong
Hong Yoon-jeong is a South Korean screenwriter best known for her work on the hit fantasy-comedy film "Miss Granny."
-
B.
Kim Min-hee
Kim Min-hee is a South Korean actress acclaimed for her nuanced performances in both mainstream and arthouse cinema, particularly in collaboration with director Hong Sang-soo.
-
C.
Eun Soo-kyung
Eun Soo-kyung is a South Korean film editor known for her work on notable contemporary Korean cinema, including the 2010 thriller "The Housemaid."
-
D.
Jeon Hye-jin
Jeon Hye-jin is a South Korean actress known for her work in film and television.
-
E.
Seol Hye-in
Seol Hye-in is a South Korean actress known for her role in the coming-of-age film "The World of Us."
- 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: Kim Soo-mi Triple: [Sandglass, hasCastMember, Kim Soo-mi]
Generated description
Kim Soo-mi is a veteran South Korean actress renowned for her prolific film and television career, particularly in comedy and family dramas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kim Soo-mi Target entity description: Kim Soo-mi is a veteran South Korean actress renowned for her prolific film and television career, particularly in comedy and family dramas.
-
A.
Hong Yoon-jeong
Hong Yoon-jeong is a South Korean screenwriter best known for her work on the hit fantasy-comedy film "Miss Granny."
-
B.
Kim Min-hee
Kim Min-hee is a South Korean actress acclaimed for her nuanced performances in both mainstream and arthouse cinema, particularly in collaboration with director Hong Sang-soo.
-
C.
Eun Soo-kyung
Eun Soo-kyung is a South Korean film editor known for her work on notable contemporary Korean cinema, including the 2010 thriller "The Housemaid."
-
D.
Jeon Hye-jin
Jeon Hye-jin is a South Korean actress known for her work in film and television.
-
E.
Seol Hye-in
Seol Hye-in is a South Korean actress known for her role in the coming-of-age film "The World of Us."
- 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_69e11e2c814c8190837d072789000486 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1276cab5c8190ac1236fde7e0394a |
completed | April 28, 2026, 9:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c306a9b0c8190a5c1eee977812ac6 |
completed | May 19, 2026, 9:42 a.m. |
| NEDg | Description generation | batch_6a0c3237777c8190a9c7158a79360717 |
completed | May 19, 2026, 9:49 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c32ece3148190a22881d69c42e948 |
completed | May 19, 2026, 9:52 a.m. |
Created at: April 16, 2026, 8:20 p.m.