Triple
T28382930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SEX boutique |
E718933
|
entity |
| Predicate | hadAesthetic |
P102534
|
FINISHED |
| Object | anti-establishment |
—
|
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: anti-establishment | Statement: [SEX boutique, hadAesthetic, anti-establishment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadAesthetic Context triple: [SEX boutique, hadAesthetic, anti-establishment]
-
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.
hadModel
Indicates that an entity possessed, used, or was associated with a particular model (e.g., a product, design, or version) at some point in time.
-
C.
hadCustom
Indicates that an entity previously possessed or was associated with a customized or user-defined version of something.
-
D.
hasDesign
Indicates that one entity possesses, embodies, or is characterized by a particular design associated with another entity.
-
E.
aestheticRole
Indicates the role or function something has within an aesthetic or artistic context (e.g., as artwork, decoration, or design element).
- 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_69eff6ef211081909d31d9be5f5567e6 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f64cb8054c8190a12bc4f58e7e4a61 |
completed | May 2, 2026, 7:12 p.m. |
| PD | Predicate disambiguation | batch_69f641e2f1708190b45b48d6a43c51d2 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 28, 2026, 1:08 a.m.