Triple
T19410780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Fortress |
E485579
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Park Hee-soon
Park Hee-soon is a South Korean actor known for his versatile performances in film and television, often portraying intense and complex characters.
|
E1390636
|
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: Park Hee-soon | Statement: [The Fortress, castMember, Park Hee-soon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Park Hee-soon Context triple: [The Fortress, castMember, Park Hee-soon]
-
A.
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.
-
B.
Dong Hee-seon
Dong Hee-seon is a South Korean screenwriter best known for her work on the hit fantasy-comedy film "Miss Granny."
-
C.
Jung Jang-seon
Jung Jang-seon is a South Korean politician serving as the mayor of the city of Pyeongtaek.
-
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.
Byun Hee-bong
Byun Hee-bong was a renowned South Korean actor celebrated for his versatile performances in film and television, including frequent collaborations with director Bong Joon-ho.
- 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: Park Hee-soon Triple: [The Fortress, castMember, Park Hee-soon]
Generated description
Park Hee-soon is a South Korean actor known for his versatile performances in film and television, often portraying intense and complex characters.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Park Hee-soon Target entity description: Park Hee-soon is a South Korean actor known for his versatile performances in film and television, often portraying intense and complex characters.
-
A.
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.
-
B.
Dong Hee-seon
Dong Hee-seon is a South Korean screenwriter best known for her work on the hit fantasy-comedy film "Miss Granny."
-
C.
Jung Jang-seon
Jung Jang-seon is a South Korean politician serving as the mayor of the city of Pyeongtaek.
-
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.
Byun Hee-bong
Byun Hee-bong was a renowned South Korean actor celebrated for his versatile performances in film and television, including frequent collaborations with director Bong Joon-ho.
- 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_6a07ab7e0d4081909296ab9498e7996f |
completed | May 15, 2026, 11:25 p.m. |
| NEDg | Description generation | batch_6a07acb892408190bd5e927cad3343ec |
completed | May 15, 2026, 11:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07ad2377488190b4f52c72a9a5d196 |
completed | May 15, 2026, 11:32 p.m. |
Created at: April 10, 2026, 1:37 p.m.