Triple
T38596459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bikini Girls with Machine Guns |
E934092
|
entity |
| Predicate | usedAesthetic |
P102534
|
FINISHED |
| Object | drive-in B-movie culture |
—
|
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: drive-in B-movie culture | Statement: [Bikini Girls with Machine Guns, usedAesthetic, drive-in B-movie culture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedAesthetic Context triple: [Bikini Girls with Machine Guns, usedAesthetic, drive-in B-movie culture]
-
A.
associatedAesthetic
chosen
Indicates a relationship where one entity is linked to or characterized by a particular aesthetic style, quality, or visual/theme-based sensibility.
-
B.
usedStyle
Indicates that one entity employed or applied a particular style, method, or manner associated with another entity.
-
C.
hasCosmeticUse
Indicates that something is used for cosmetic purposes, such as enhancing or altering appearance.
-
D.
usedWithStyle
Indicates that something is employed or applied in conjunction with a particular style or stylistic manner.
-
E.
usesAsStyleOf
Indicates that one entity adopts or applies another entity as a stylistic model, method, or manner of expression.
- 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_69f76ecc17688190b389b693a5927501 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69ff2d22ffb48190ae58ddf3c7e02869 |
completed | May 9, 2026, 12:48 p.m. |
| PD | Predicate disambiguation | batch_69ff2ac2e1c4819096cc64e94aef2ff0 |
completed | May 9, 2026, 12:38 p.m. |
Created at: May 3, 2026, 4:32 p.m.