Triple
T21869354
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oldboy |
E539962
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Kang Hye-jung |
—
|
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: Kang Hye-jung | Statement: [Oldboy, castMember, Kang Hye-jung]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kang Hye-jung Context triple: [Oldboy, castMember, Kang Hye-jung]
-
A.
Kang Hye-jung
chosen
Kang Hye-jung is a South Korean actress acclaimed for her intense and versatile performances in film and television, notably in the psychological thriller "Oldboy."
-
B.
Shin Hye-sook
Shin Hye-sook is a South Korean figure skating coach best known for working with Olympic champion Yuna Kim during her early development.
-
C.
Lee Hae-jin
Lee Hae-jin is a South Korean entrepreneur and technologist best known as the founder and longtime leader of internet giant Naver Corporation.
-
D.
Kim Hye-ja
Kim Hye-ja is a renowned South Korean actress, especially acclaimed for her powerful performance in Bong Joon-ho’s thriller "Mother" and her long career in television dramas.
-
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.