Triple
T4525812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Lazenby |
E103373
|
entity |
| Predicate | castingFact |
P18965
|
FINISHED |
| Object | cast as James Bond despite limited prior acting experience |
—
|
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: cast as James Bond despite limited prior acting experience | Statement: [George Lazenby, castingFact, cast as James Bond despite limited prior acting experience]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: castingFact Context triple: [George Lazenby, castingFact, cast as James Bond despite limited prior acting experience]
-
A.
cast
chosen
Indicates that an agent selects and assigns a person or thing to play a specific role or function in a production or context.
-
B.
castIn
Indicates that an actor or performer appears in a particular film, show, or production.
-
C.
castingReason
Indicates the reason or justification for selecting or assigning a particular entity (e.g., a person or object) to a specific role, function, or category.
-
D.
typicalCasting
Indicates that one entity is the usual or standard casting choice for portraying another entity (such as a role, character, or type).
-
E.
castingType
Indicates the specific method or category of casting used to transform or represent one entity in terms of another.
- 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_69bd43dba59881908cf59b31df8c7ae1 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd577490f48190ac1fb3cbf3d8a41e |
completed | March 20, 2026, 2:19 p.m. |
| PD | Predicate disambiguation | batch_69bd521cf77c819083852de3094d1377 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:03 p.m.