Triple
T35248915
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akure dialect of Yoruba |
E1018041
|
entity |
| Predicate | usesToneForLexicalContrast |
P150897
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Akure dialect of Yoruba, usesToneForLexicalContrast, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesToneForLexicalContrast Context triple: [Akure dialect of Yoruba, usesToneForLexicalContrast, yes]
-
A.
tonalContrast
Indicates a relationship where two elements differ in their tonal qualities (such as lightness, darkness, or color value) to create visual distinction or emphasis.
-
B.
hasTonalityShift
Indicates a change in the tonal quality, mood, or key within a piece or segment, marking a shift from one tonality to another.
-
C.
contributesToTone
Indicates that one entity plays a role in shaping, influencing, or determining the overall tone or mood of another entity.
-
D.
inTonality
Indicates that something (such as a musical element, passage, or piece) is expressed, structured, or interpreted within a specific musical key or tonal framework.
-
E.
hasToneContrast
chosen
Indicates a relationship where two tones differ in pitch, contour, or phonological features such that they form a perceptible tonal contrast.
- 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_69f76de407d081909dfc3c419817ae93 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fcf825ca7081909d06b0df33eb33f9 |
completed | May 7, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69fcf42160f0819096812a8bf590875e |
completed | May 7, 2026, 8:20 p.m. |
Created at: May 3, 2026, 4:02 p.m.