Triple
T12449221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Holdovers |
E297484
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Jim Kaplan
Jim Kaplan is an actor known for his role in the 2023 film "The Holdovers."
|
E993474
|
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: Jim Kaplan | Statement: [The Holdovers, castMember, Jim Kaplan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jim Kaplan Context triple: [The Holdovers, castMember, Jim Kaplan]
-
A.
Larry Kaplan
Larry Kaplan is a pioneering video game designer and programmer best known as one of the co-founders of Activision and an early developer for the Atari 2600.
-
B.
Billy Kaplan
Billy Kaplan is a Marvel Comics superhero, also known as Wiccan, who is a powerful magic user and a member of the Young Avengers.
-
C.
Greg Kaplan
Greg Kaplan is an economist known for his research on household heterogeneity, consumption, and macroeconomic policy, and for his contributions to modern macroeconomic modeling.
-
D.
Sol Kaplan
Sol Kaplan was an American composer best known for his film and television scores, including work in mid-20th-century Hollywood.
-
E.
Hank Kaplan
Hank Kaplan is a fictional character from the American medical drama television series "Nurses."
- 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: Jim Kaplan Triple: [The Holdovers, castMember, Jim Kaplan]
Generated description
Jim Kaplan is an actor known for his role in the 2023 film "The Holdovers."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jim Kaplan Target entity description: Jim Kaplan is an actor known for his role in the 2023 film "The Holdovers."
-
A.
Larry Kaplan
Larry Kaplan is a pioneering video game designer and programmer best known as one of the co-founders of Activision and an early developer for the Atari 2600.
-
B.
Billy Kaplan
Billy Kaplan is a Marvel Comics superhero, also known as Wiccan, who is a powerful magic user and a member of the Young Avengers.
-
C.
Greg Kaplan
Greg Kaplan is an economist known for his research on household heterogeneity, consumption, and macroeconomic policy, and for his contributions to modern macroeconomic modeling.
-
D.
Sol Kaplan
Sol Kaplan was an American composer best known for his film and television scores, including work in mid-20th-century Hollywood.
-
E.
Hank Kaplan
Hank Kaplan is a fictional character from the American medical drama television series "Nurses."
- 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_69d6ada166c48190b902972cd2408fa3 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d9e592c81908cf7f3ca170d942c |
completed | April 10, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ea710d481908371209cb92502a6 |
completed | May 2, 2026, 8:29 p.m. |
| NEDg | Description generation | batch_69f65fadc97081908376913e390cfc3d |
completed | May 2, 2026, 8:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f660c3d914819097b57784889ca389 |
completed | May 2, 2026, 8:38 p.m. |
Created at: April 8, 2026, 9:56 p.m.