Triple
T21869454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Train to Busan |
E539964
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Yang Jin-mo |
—
|
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: Yang Jin-mo | Statement: [Train to Busan, editedBy, Yang Jin-mo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yang Jin-mo Context triple: [Train to Busan, editedBy, Yang Jin-mo]
-
A.
Yang Jin-mo
chosen
Yang Jin-mo is a South Korean film editor best known for his acclaimed work on the Academy Award–winning film "Parasite."
-
B.
Lee Jin
Lee Jin is a film editor known for working on the acclaimed South Korean drama film "Ode to My Father."
-
C.
Yoon Jin-seo
Yoon Jin-seo is a South Korean actress best known internationally for her supporting role in the acclaimed thriller film "Oldboy."
-
D.
Yen Ji-dan
Yen Ji-dan is the birth name of Donnie Yen, a renowned Hong Kong actor, martial artist, and action film director known for his roles in movies such as the "Ip Man" series.
-
E.
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."
- 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_69e0c478f59081909d54302b57fc1ce3 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0f334362c819094af465ee57b47e6 |
completed | April 28, 2026, 5:49 p.m. |
Created at: April 16, 2026, 6:57 p.m.