Triple
T19410717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Granny |
E485578
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Dong Hee-seon
Dong Hee-seon is a South Korean screenwriter best known for her work on the hit fantasy-comedy film "Miss Granny."
|
E1383034
|
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: Dong Hee-seon | Statement: [Miss Granny, screenwriter, Dong Hee-seon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dong Hee-seon Context triple: [Miss Granny, screenwriter, Dong Hee-seon]
-
A.
Jung Jang-seon
Jung Jang-seon is a South Korean politician serving as the mayor of the city of Pyeongtaek.
-
B.
Yang Hak-seon
Yang Hak-seon is a South Korean artistic gymnast renowned as an Olympic and world champion vaulter and the first South Korean gymnast to win an Olympic gold medal.
-
C.
Suh Kyung-bae
Suh Kyung-bae is a South Korean billionaire businessman best known as the chairman of Amorepacific Corporation, one of Asia’s leading cosmetics companies.
-
D.
Won In-choul
Won In-choul is a South Korean Air Force general who served as the country’s Chairman of the Joint Chiefs of Staff.
-
E.
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.
- 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: Dong Hee-seon Triple: [Miss Granny, screenwriter, Dong Hee-seon]
Generated description
Dong Hee-seon is a South Korean screenwriter best known for her work on the hit fantasy-comedy film "Miss Granny."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dong Hee-seon Target entity description: Dong Hee-seon is a South Korean screenwriter best known for her work on the hit fantasy-comedy film "Miss Granny."
-
A.
Jung Jang-seon
Jung Jang-seon is a South Korean politician serving as the mayor of the city of Pyeongtaek.
-
B.
Yang Hak-seon
Yang Hak-seon is a South Korean artistic gymnast renowned as an Olympic and world champion vaulter and the first South Korean gymnast to win an Olympic gold medal.
-
C.
Suh Kyung-bae
Suh Kyung-bae is a South Korean billionaire businessman best known as the chairman of Amorepacific Corporation, one of Asia’s leading cosmetics companies.
-
D.
Won In-choul
Won In-choul is a South Korean Air Force general who served as the country’s Chairman of the Joint Chiefs of Staff.
-
E.
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.
- 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_69d8e8d5162481909db12435d9535c1a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e62af4cc0c81909056b5e2ee574ab1 |
completed | April 20, 2026, 1:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07576b51e08190b70519021918b280 |
completed | May 15, 2026, 5:27 p.m. |
| NEDg | Description generation | batch_6a07588550588190963ebb3c742d4d3f |
completed | May 15, 2026, 5:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07592c42488190b140e501dd9d4ae1 |
completed | May 15, 2026, 5:34 p.m. |
Created at: April 10, 2026, 1:37 p.m.