Triple
T18669424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ray Duquette |
E456430
|
entity |
| Predicate | hasOnScreenNudity |
P101247
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Ray Duquette, hasOnScreenNudity, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOnScreenNudity Context triple: [Ray Duquette, hasOnScreenNudity, yes]
-
A.
nudity
chosen
Indicates that an entity is unclothed or exposes parts of the body typically covered, representing a state or depiction of being nude.
-
B.
depictsSex
Indicates that one entity visually represents or portrays sexual activity or sexual content involving another entity.
-
C.
containsAdultContent
Indicates that the referenced item includes material intended for adults, such as explicit sexual, violent, or otherwise age-restricted content.
-
D.
hasRomanticSceneAt
Indicates that a romantic scene occurs at a specific location or point in time within a work or context.
-
E.
hasNSFWPolicy
Indicates that an entity has an established policy governing the handling, display, or treatment of not-safe-for-work (NSFW) content.
- 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_69d8d38f72b4819090a935175d9ca8af |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e556b0502881909ea05f2746163746 |
completed | April 19, 2026, 10:26 p.m. |
| PD | Predicate disambiguation | batch_69e478db7a248190a8c6584673773923 |
completed | April 19, 2026, 6:40 a.m. |
Created at: April 10, 2026, 11:48 a.m.