Triple
T20796191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Memories of Murder |
E511914
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Kim Roe-ha
Kim Roe-ha is a South Korean actor known for his character roles in films and television dramas, including the acclaimed crime thriller "Memories of Murder."
|
E1498965
|
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 Roe-ha | Statement: [Memories of Murder, hasCastMember, Kim Roe-ha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kim Roe-ha Context triple: [Memories of Murder, hasCastMember, Kim Roe-ha]
-
A.
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.
-
B.
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.
-
C.
Dong Hee-seon
Dong Hee-seon is a South Korean screenwriter best known for her work on the hit fantasy-comedy film "Miss Granny."
-
D.
Lee Hak-rae
Lee Hak-rae is a South Korean sports official best known for delivering the judges' oath at the 1988 Seoul Summer Olympics.
-
E.
Kim Gae-nam
Kim Gae-nam is an individual whose specific public background or notable achievements are not clearly documented in widely available sources.
- 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 Roe-ha Triple: [Memories of Murder, hasCastMember, Kim Roe-ha]
Generated description
Kim Roe-ha is a South Korean actor known for his character roles in films and television dramas, including the acclaimed crime thriller "Memories of Murder."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kim Roe-ha Target entity description: Kim Roe-ha is a South Korean actor known for his character roles in films and television dramas, including the acclaimed crime thriller "Memories of Murder."
-
A.
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.
-
B.
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.
-
C.
Dong Hee-seon
Dong Hee-seon is a South Korean screenwriter best known for her work on the hit fantasy-comedy film "Miss Granny."
-
D.
Lee Hak-rae
Lee Hak-rae is a South Korean sports official best known for delivering the judges' oath at the 1988 Seoul Summer Olympics.
-
E.
Kim Gae-nam
Kim Gae-nam is an individual whose specific public background or notable achievements are not clearly documented in widely available sources.
- 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_6a0a2eb705288190a77bdd249e57f869 |
completed | May 17, 2026, 9:10 p.m. |
| NEDg | Description generation | batch_6a0a2f52c2688190bf05774546a4ba58 |
completed | May 17, 2026, 9:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a2fe468ac81908b591a086eade238 |
completed | May 17, 2026, 9:15 p.m. |
Created at: April 16, 2026, 12:39 p.m.