Triple
T19411178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sea Fog |
E485590
|
entity |
| Predicate | filmEditingBy |
P14416
|
FINISHED |
| Object | Kim Sang-bum |
—
|
NE NERFINISHED |
How this triple was built (2 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 Sang-bum | Statement: [Sea Fog, filmEditingBy, Kim Sang-bum]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kim Sang-bum Context triple: [Sea Fog, filmEditingBy, Kim Sang-bum]
-
A.
Kim Sang-bum
chosen
Kim Sang-bum is a South Korean film editor known for his work on numerous acclaimed Korean movies.
-
B.
Kim Sang-ho
Kim Sang-ho is a South Korean actor known for his versatile supporting roles in films and television dramas.
-
C.
Kim Jeong-suk
Kim Jeong-suk is best known as the wife of South Korean general Paik Sun-yup, a prominent military figure during and after the Korean War.
-
D.
Kim Hong-gul
Kim Hong-gul is a South Korean politician and the son of former President and Nobel Peace Prize laureate Kim Dae-jung.
-
E.
Kim Sang-hun
Kim Sang-hun is a central fictional figure in the historical Korean film "The Fortress," which portrays the moral and political struggles of Joseon officials during the Qing invasion.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e62af681288190ba2ec52d5adb6a22 |
completed | April 20, 2026, 1:32 p.m. |
Created at: April 10, 2026, 1:37 p.m.