Triple
T3250504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Chair |
E68164
|
entity |
| Predicate | mainCharacter |
P1183
|
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: [The Chair, mainCharacter, Ji-Yoon Kim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ji-Yoon Kim Context triple: [The Chair, mainCharacter, 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.
Dongjin Seo
Dongjin Seo is a neuroscientist and engineer known as one of the co-founders of the brain–computer interface company Neuralink.
-
C.
Jun-Ho Oh
Jun-Ho Oh is a South Korean roboticist best known for leading the development of the humanoid robot DRC-HUBO that won the DARPA Robotics Challenge.
-
D.
Hyein Park
Hyein Park is a Korean-Canadian voice actress best known for voicing the character Abby in Pixar’s animated film "Turning Red."
-
E.
Kwanghun Chung
Kwanghun Chung is a neuroscientist and bioengineer known for pioneering advanced tissue-clearing and imaging techniques that enable high-resolution, three-dimensional visualization of biological tissues.
- 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_69ad858e4c708190aa31d486cfee8a6a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaf40f7908190a450c3136fccb020 |
completed | March 8, 2026, 5:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2e82f91788190a9b14613eab7a439 |
completed | March 12, 2026, 4:22 p.m. |
Created at: March 8, 2026, 3:09 p.m.