Triple
T20816601
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bolero (1984 film) |
E512454
|
entity |
| Predicate | featuresNudity |
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: [Bolero (1984 film), featuresNudity, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresNudity Context triple: [Bolero (1984 film), featuresNudity, 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.
featuresMusicVideoNudity
Indicates that the music video contains depictions of nudity.
-
D.
hasSexualityCharacteristic
Indicates that an entity possesses a specific sexual orientation or sexuality-related characteristic.
-
E.
containsAdultContent
Indicates that the referenced item includes material intended for adults, such as explicit sexual, violent, or otherwise age-restricted 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_69e0b4cd25088190b48ca9700cd24efc |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2f3473c81908c43a2ec242b1acd |
completed | April 21, 2026, 12:21 a.m. |
| PD | Predicate disambiguation | batch_69e5c99ca55481908e8d434fa901cfd6 |
completed | April 20, 2026, 6:37 a.m. |
Created at: April 16, 2026, 12:41 p.m.