Triple
T20796279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mother (2009 film) |
E511916
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Moon Yang-kwon
Moon Yang-kwon is a South Korean film producer best known for his work on acclaimed movies such as the 2009 thriller-drama "Mother."
|
E1503815
|
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: Moon Yang-kwon | Statement: [Mother (2009 film), producer, Moon Yang-kwon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moon Yang-kwon Context triple: [Mother (2009 film), producer, Moon Yang-kwon]
-
A.
Yuk Young-soo
Yuk Young-soo was the respected First Lady of South Korea and wife of President Park Chung-hee, remembered for her charitable work and her assassination in 1974.
-
B.
Koh Sang-ji
Koh Sang-ji is a notable individual bearing the Korean surname Koh, recognized enough to be specifically cited among its prominent bearers.
-
C.
Jung Jang-seon
Jung Jang-seon is a South Korean politician serving as the mayor of the city of Pyeongtaek.
-
D.
Suh Jin-suk
Suh Jin-suk is a notable individual who prominently bears the Korean surname Suh.
-
E.
Dong Hee-seon
Dong Hee-seon is a South Korean screenwriter best known for her work on the hit fantasy-comedy film "Miss Granny."
- 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: Moon Yang-kwon Triple: [Mother (2009 film), producer, Moon Yang-kwon]
Generated description
Moon Yang-kwon is a South Korean film producer best known for his work on acclaimed movies such as the 2009 thriller-drama "Mother."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Moon Yang-kwon Target entity description: Moon Yang-kwon is a South Korean film producer best known for his work on acclaimed movies such as the 2009 thriller-drama "Mother."
-
A.
Yuk Young-soo
Yuk Young-soo was the respected First Lady of South Korea and wife of President Park Chung-hee, remembered for her charitable work and her assassination in 1974.
-
B.
Koh Sang-ji
Koh Sang-ji is a notable individual bearing the Korean surname Koh, recognized enough to be specifically cited among its prominent bearers.
-
C.
Jung Jang-seon
Jung Jang-seon is a South Korean politician serving as the mayor of the city of Pyeongtaek.
-
D.
Suh Jin-suk
Suh Jin-suk is a notable individual who prominently bears the Korean surname Suh.
-
E.
Dong Hee-seon
Dong Hee-seon is a South Korean screenwriter best known for her work on the hit fantasy-comedy film "Miss Granny."
- 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_69e0b4cb83948190bd57bec21d78ed53 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2ad6f0481909e0bab7119f10f9c |
completed | April 21, 2026, 12:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a45c718208190939b58a14d5ab366 |
completed | May 17, 2026, 10:48 p.m. |
| NEDg | Description generation | batch_6a0a47375518819091234278dfc326eb |
completed | May 17, 2026, 10:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a47a51cb88190856282fdfa56460b |
completed | May 17, 2026, 10:56 p.m. |
Created at: April 16, 2026, 12:39 p.m.