Triple
T6061200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yuna Kim |
E135035
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Yuna Kim |
E135035
|
NE FINISHED |
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: Yuna Kim | Statement: [Yuna Kim, name, Yuna Kim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yuna Kim Context triple: [Yuna Kim, name, Yuna Kim]
-
A.
Yuna Kim
chosen
Yuna Kim is a South Korean figure skating legend and Olympic champion widely celebrated for her technical excellence, artistry, and global impact on the sport.
-
B.
Alice Kim
Alice Kim is an American former waitress and actress best known as the ex-wife of actor Nicolas Cage.
-
C.
Nellie Kim
Nellie Kim is a former Soviet artistic gymnast renowned for her multiple Olympic gold medals in the 1970s and for pioneering difficult tumbling and vaulting skills.
-
D.
Jane Kim
Jane Kim is an American politician and attorney who served on the San Francisco Board of Supervisors and is known for her progressive advocacy on housing, education, and workers’ rights.
-
E.
Jung Kim
Jung Kim is the charismatic and quick-witted convenience store manager and son in the Canadian sitcom "Kim's Convenience."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c00878d06881909ee78e88913bf890 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0571fcecc8190a68e0d0668bbbfa7 |
completed | March 22, 2026, 8:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11d197dd08190bcc6904c7c2e41aa |
completed | March 23, 2026, 10:59 a.m. |
Created at: March 22, 2026, 4:10 p.m.