Triple
T32363321
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roses (Imanbek Remix) |
E826922
|
entity |
| Predicate | influencedTrend |
P157823
|
FINISHED |
| Object | slap house mainstream popularity |
—
|
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: slap house mainstream popularity | Statement: [Roses (Imanbek Remix), influencedTrend, slap house mainstream popularity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: influencedTrend Context triple: [Roses (Imanbek Remix), influencedTrend, slap house mainstream popularity]
-
A.
influencedFashionTrend
Indicates that one entity caused or contributed to a change or direction in another entity’s fashion style or prevailing clothing trends.
-
B.
influencedIn
Indicates that one entity had an effect on or shaped another entity within a specific context, domain, or setting.
-
C.
influenceOf
Indicates that one entity affects, shapes, or alters the state, behavior, or properties of another entity.
-
D.
influenced
Indicates that one entity has affected, shaped, or altered another entity’s state, behavior, or characteristics.
-
E.
influencedAspectOf
chosen
Indicates that one entity has affected, shaped, or altered a particular aspect or component of another entity.
- 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_69f349166d548190887b412fe908e2f4 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f9fd6834cc8190aa27153d6a99f3bb |
completed | May 5, 2026, 2:23 p.m. |
| PD | Predicate disambiguation | batch_69f7cf769338819092a5f42653dcc956 |
completed | May 3, 2026, 10:43 p.m. |
Created at: May 1, 2026, 12:50 a.m.