Triple
T198982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbenheimer phenomenon |
E4059
|
entity |
| Predicate | themeContrast |
P7994
|
FINISHED |
| Object | bright pink aesthetic |
—
|
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: bright pink aesthetic | Statement: [Barbenheimer phenomenon, themeContrast, bright pink aesthetic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: themeContrast Context triple: [Barbenheimer phenomenon, themeContrast, bright pink aesthetic]
-
A.
theme
Indicates the entity that is the primary participant or content affected or characterized by an action, event, or state.
-
B.
themeExamples
Indicates that the related entity serves as an example or illustration of the theme expressed by the subject.
-
C.
colors
Indicates that one entity assigns, describes, or provides the color or colors of another entity.
-
D.
hasCentralTheme
Indicates that one entity serves as the primary or dominant theme or subject matter of another entity.
-
E.
blackAndWhite
Indicates that something is presented or exists in only black and white, without any other colors.
- F. None of above. chosen
Provenance (4 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_69a254bca59881909a15e1496f1508c7 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25bcb2c7c8190b0e031e93651182a |
completed | Feb. 28, 2026, 3:06 a.m. |
| PD | Predicate disambiguation | batch_69a25b4886b48190b46fd2244648a098 |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25bc6ba208190aa8bec59d32f95fd |
completed | Feb. 28, 2026, 3:06 a.m. |
Created at: Feb. 28, 2026, 2:44 a.m.