Triple
T7996282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gloria Vanderbilt designer jeans |
E186133
|
entity |
| Predicate | partOfTrend |
P22393
|
FINISHED |
| Object | designer jeans craze of the late 1970s |
—
|
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: designer jeans craze of the late 1970s | Statement: [Gloria Vanderbilt designer jeans, partOfTrend, designer jeans craze of the late 1970s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfTrend Context triple: [Gloria Vanderbilt designer jeans, partOfTrend, designer jeans craze of the late 1970s]
-
A.
trends
Indicates that one entity exhibits a general direction of change or development over time in relation to another reference or context.
-
B.
trendy
Indicates that something is currently fashionable, popular, or in line with prevailing styles or tastes.
-
C.
hasTrend
Indicates that something exhibits or is associated with a particular pattern of change or direction over time.
-
D.
socialPhenomenon
Indicates a relationship where an event, behavior, or pattern emerges from and affects interactions within a society or group.
-
E.
designTrend
chosen
Indicates a prevailing or emerging stylistic direction or pattern that influences how something is designed over a period of time.
- 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_69ca829c6c308190ab05b43d234c52b2 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c97968481908d261b3f0bd6b8e6 |
completed | March 31, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69cb0483d3b48190b250c7603d747bca |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:17 p.m.