Triple
T18441397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rashaida people |
E450534
|
entity |
| Predicate | dressStyleInfluence |
P109518
|
FINISHED |
| Object | Arabian Peninsula styles |
—
|
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: Arabian Peninsula styles | Statement: [Rashaida people, dressStyleInfluence, Arabian Peninsula styles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dressStyleInfluence Context triple: [Rashaida people, dressStyleInfluence, Arabian Peninsula styles]
-
A.
influencedFashionTrend
chosen
Indicates that one entity caused or contributed to a change or direction in another entity’s fashion style or prevailing clothing trends.
-
B.
fashionStyle
Indicates the characteristic way in which an entity dresses or presents themselves in terms of clothing and appearance.
-
C.
personHasNotableStyle
Indicates that a person is recognized for having a distinctive or noteworthy style.
-
D.
styleTendsTo
Indicates that one style is generally inclined or likely to develop, appear, or be adopted in the direction of another style.
-
E.
styleDiversity
Indicates variation in the manner, form, or approach with which an entity presents, performs, or expresses something across different instances or contexts.
- 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_69d8d381d6388190a9e94e9c658174e4 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e51c10a86c819091196968b648fc92 |
completed | April 19, 2026, 6:16 p.m. |
| PD | Predicate disambiguation | batch_69e469c943a4819094c8fdc5971ad3a7 |
completed | April 19, 2026, 5:36 a.m. |
Created at: April 10, 2026, 11:30 a.m.