Triple
T14530895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yaz McKay |
E340911
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Ji-Yoon Kim |
E341851
|
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: Ji-Yoon Kim | Statement: [Yaz McKay, associatedWith, Ji-Yoon Kim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ji-Yoon Kim Context triple: [Yaz McKay, associatedWith, Ji-Yoon Kim]
-
A.
Ji-Yoon Kim
chosen
Ji-Yoon Kim is the beleaguered yet determined new chair of a struggling university English department in the Netflix dramedy "The Chair," juggling academic politics, cultural change, and single motherhood.
-
B.
Jae-on Kim
Jae-on Kim is a political scientist known for his work on democratic participation and political equality.
-
C.
Wookyung Jung
Wookyung Jung is a film producer best known for working on the animated feature "The Nut Job."
-
D.
Soo-Yung Han
Soo-Yung Han is the young daughter of a Chinese consul whose kidnapping repeatedly drives the central plot and emotional stakes of the Rush Hour film series.
-
E.
Sunmin Park
Sunmin Park is a film producer best known for her work on the psychological horror movie "The Others."
- 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_69d822dac79c8190a84a073f3cbaced5 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dea052d01c81909c8592c351be6f35 |
completed | April 14, 2026, 8:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fda9107e9c8190850716c21e627a39 |
completed | May 8, 2026, 9:12 a.m. |
Created at: April 10, 2026, 1:22 a.m.