Triple
T30935670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paul Van Doren |
E788115
|
entity |
| Predicate | hasBrandImpact |
P180056
|
FINISHED |
| Object | iconic status in skate 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: iconic status in skate culture | Statement: [Paul Van Doren, hasBrandImpact, iconic status in skate culture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBrandImpact Context triple: [Paul Van Doren, hasBrandImpact, iconic status in skate culture]
-
A.
hasVisualImpact
Indicates that one entity affects or influences the visual appearance or aesthetic perception of another.
-
B.
hasImpactScale
Indicates the degree or magnitude of impact that one entity or action has on another, typically expressed along a defined scale.
-
C.
hasBrandReputation
Indicates that an entity possesses a certain level or type of perceived quality, trustworthiness, or public image associated with its brand.
-
D.
hasBrandRecognitionFor
Indicates that one entity is aware of, recognizes, or can identify the brand of another entity.
-
E.
hasBrandRole
Indicates that an entity holds a specific functional or organizational role in relation to a particular brand.
- 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_69f224c0b7fc819090cb89df60d23653 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f7308a096081909d66a56f3c926806 |
completed | May 3, 2026, 11:24 a.m. |
| PD | Predicate disambiguation | batch_69f72a00c5f081908b6539d15baf4e12 |
completed | May 3, 2026, 10:57 a.m. |
| PDg | Predicate description generation | batch_69f730890a008190a882f7828f1c9162 |
completed | May 3, 2026, 11:24 a.m. |
Created at: April 29, 2026, 8:52 p.m.