Triple
T16003585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Russian Roulette |
E388153
|
entity |
| Predicate | isOftenDepictedIn |
P58519
|
FINISHED |
| Object | films |
—
|
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: films | Statement: [Russian Roulette, isOftenDepictedIn, films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isOftenDepictedIn Context triple: [Russian Roulette, isOftenDepictedIn, films]
-
A.
commonlyDepictedOn
Indicates that something is frequently shown or represented on the surface, medium, or context of another thing.
-
B.
oftenDepictedAs
Indicates that one entity is frequently represented or portrayed in the form, appearance, or symbolism of another entity.
-
C.
workOftenDepicts
chosen
Indicates that one entity’s work frequently portrays, represents, or includes the other entity as a subject or theme.
-
D.
appearsInWorkOfArt
Indicates that an entity is depicted, represented, or otherwise featured within a particular work of art.
-
E.
imageDepictedIn
Indicates that a particular image is shown, represented, or included within another resource or context.
- 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142dc081c819082527e3fa8773460 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:55 a.m.