Triple
T9790011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Nut Job |
E237582
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Kyoungwon Lim
Kyoungwon Lim is a film producer best known for working on the animated feature "The Nut Job."
|
E823791
|
NE FINISHED |
How this triple was built (4 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: Kyoungwon Lim | Statement: [The Nut Job, producer, Kyoungwon Lim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kyoungwon Lim Context triple: [The Nut Job, producer, Kyoungwon Lim]
-
A.
Jae-on Kim
Jae-on Kim is a political scientist known for his work on democratic participation and political equality.
-
B.
Yong-jun Jung
Yong-jun Jung is a notable individual recognized for achievements significant enough to be distinctly associated with the surname Jung.
-
C.
Jong Wook Kim
Jong Wook Kim is a machine learning researcher known for his contributions to multimodal models, including work on the development of CLIP at OpenAI.
-
D.
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.
-
E.
Wookyung Jung
Wookyung Jung is a film producer best known for working on the animated feature "The Nut Job."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kyoungwon Lim Triple: [The Nut Job, producer, Kyoungwon Lim]
Generated description
Kyoungwon Lim is a film producer best known for working on the animated feature "The Nut Job."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kyoungwon Lim Target entity description: Kyoungwon Lim is a film producer best known for working on the animated feature "The Nut Job."
-
A.
Jae-on Kim
Jae-on Kim is a political scientist known for his work on democratic participation and political equality.
-
B.
Yong-jun Jung
Yong-jun Jung is a notable individual recognized for achievements significant enough to be distinctly associated with the surname Jung.
-
C.
Jong Wook Kim
Jong Wook Kim is a machine learning researcher known for his contributions to multimodal models, including work on the development of CLIP at OpenAI.
-
D.
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.
-
E.
Wookyung Jung
Wookyung Jung is a film producer best known for working on the animated feature "The Nut Job."
- F. None of above. chosen
Provenance (5 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_69ca84dc04488190b9c91193976c0960 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda214875481909f39e1d4dbac1fdb |
completed | April 1, 2026, 10:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1cc4e30bc81909b1dce4a0cc69991 |
completed | April 5, 2026, 2:43 a.m. |
| NEDg | Description generation | batch_69d1cdc7b8c48190bb5fc96bb93dfc2d |
completed | April 5, 2026, 2:49 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1ce1aead081908da4a85ded350c17 |
completed | April 5, 2026, 2:51 a.m. |
Created at: March 30, 2026, 8:28 p.m.