Triple
T32362977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | My Power |
E826913
|
entity |
| Predicate | visualContext |
P29429
|
FINISHED |
| Object | African-inspired fashion and choreography |
—
|
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: African-inspired fashion and choreography | Statement: [My Power, visualContext, African-inspired fashion and choreography]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visualContext Context triple: [My Power, visualContext, African-inspired fashion and choreography]
-
A.
visualField
Indicates the spatial region in which a visual system or observer can detect and perceive visual stimuli.
-
B.
visualCompanion
Indicates that one entity serves as a visual counterpart, partner, or accompanying element to another in a visual context.
-
C.
visualExperience
Indicates a relationship where an entity undergoes or has a particular experience involving visual perception or seeing.
-
D.
visualElements
chosen
Indicates that one entity contains, uses, or is characterized by specific visual components or graphical features associated with another entity.
-
E.
vision
Indicates that an entity perceives another entity or object visually, using sight.
- 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_69f6be9be6b48190b164c572650e8b95 |
completed | May 3, 2026, 3:18 a.m. |
| PD | Predicate disambiguation | batch_69f6ba6cef208190bc5cd43d96127004 |
completed | May 3, 2026, 3:01 a.m. |
Created at: May 1, 2026, 12:50 a.m.