Triple
T21869483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Train to Busan |
E539964
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Shim Eun-kyung |
—
|
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: Shim Eun-kyung | Statement: [Train to Busan, portrayedBy, Shim Eun-kyung]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shim Eun-kyung Context triple: [Train to Busan, portrayedBy, Shim Eun-kyung]
-
A.
Shim Eun-kyung
chosen
Shim Eun-kyung is a South Korean actress known for her versatile performances in film and television, particularly in comedic and dramatic roles.
-
B.
Yoon Ga-eun
Yoon Ga-eun is a South Korean film director and screenwriter known for her sensitive, realistic portrayals of children and adolescence.
-
C.
Jeong Eun-ji
Jeong Eun-ji is a South Korean singer and actress best known as the main vocalist of the girl group Apink and for her roles in popular television dramas.
-
D.
Kang Eun-ji
Kang Eun-ji is a South Korean individual known primarily in relation to Seong Ga-yeong, likely as a family member within the same public or entertainment circle.
-
E.
Won Jin-ah
Won Jin-ah is a South Korean actress known for her roles in television dramas and films, including the dark fantasy series "Hellbound."
- 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.