Triple
T23480653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Stack as Rex Kramer |
E570394
|
entity |
| Predicate | castingContrast |
P40635
|
FINISHED |
| Object | serious dramatic actor in absurd comedic role |
—
|
LITERAL 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: serious dramatic actor in absurd comedic role | Statement: [Robert Stack as Rex Kramer, castingContrast, serious dramatic actor in absurd comedic role]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: castingContrast Context triple: [Robert Stack as Rex Kramer, castingContrast, serious dramatic actor in absurd comedic role]
-
A.
achievesContrast
Indicates that one entity creates or enhances a visual or conceptual difference relative to another entity.
-
B.
createsContrastIn
chosen
Indicates a relationship where one element is used to highlight or emphasize differences with another element within a given context.
-
C.
castingType
Indicates the specific method or category of casting used to transform or represent one entity in terms of another.
-
D.
castType
Indicates the specific kind or category of casting relationship that exists between two entities.
-
E.
castIn
Indicates that an actor or performer appears in a particular film, show, or production.
- F. None of above.
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_69e245af8a88819084f2704f6d265a92 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a75002008190b02fbffd94e5e8b1 |
completed | April 29, 2026, 6:38 a.m. |
| PD | Predicate disambiguation | batch_69f0620ac3608190b36916261ea50f54 |
completed | April 28, 2026, 7:30 a.m. |
Created at: April 17, 2026, 6:03 p.m.