Triple
T20388208
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beef |
E498012
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Lee Sung Jin |
—
|
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: Lee Sung Jin | Statement: [Beef, executiveProducer, Lee Sung Jin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lee Sung Jin Context triple: [Beef, executiveProducer, Lee Sung Jin]
-
A.
Lee Sung Jin
chosen
Lee Sung Jin is a Korean-American writer, producer, and director best known for creating the acclaimed Netflix dark comedy-drama series "Beef."
-
B.
Lee Yong-ik
Lee Yong-ik was a prominent Korean educator and nationalist who played a key role in modernizing education in Korea and helped establish Korea University as a leading institution of higher learning.
-
C.
Lee Tae-hun
Lee Tae-hun is a South Korean film producer known for his work on the international release of the science fiction thriller "Snowpiercer."
-
D.
Jo Woo-jin
Jo Woo-jin is a South Korean actor known for his versatile supporting and character roles in films and television dramas.
-
E.
Lee Woo-jin
Lee Woo-jin is the enigmatic and vengeful antagonist of the South Korean neo-noir film "Oldboy," orchestrating an elaborate revenge plot against the protagonist.
- 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_69e0b4a71ebc8190b153a36c738730f4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6790d9e5881908bde7da9e5e541a0 |
completed | April 20, 2026, 7:05 p.m. |
Created at: April 16, 2026, 11:28 a.m.