Triple
T24732366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guyana Wapishana |
E618319
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | variety of Wapishana language |
C49085
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: variety of Wapishana language Context triple: [Guyana Wapishana, instanceOf, variety of Wapishana language]
-
A.
variety of Lau language
A variety of Lau language is a distinct regional or social form of the Lau language characterized by unique phonological, lexical, or grammatical features while remaining mutually intelligible with other Lau forms.
-
B.
variety of Swahili
A variety of Swahili is a distinct form of the Swahili language characterized by specific regional, social, or functional linguistic features in its phonology, grammar, and vocabulary.
-
C.
Mari language variety
A Mari language variety is a specific form or dialect of the Mari language, distinguished by its unique phonological, grammatical, and lexical features used by a particular Mari-speaking community.
-
D.
variety of the Uma language
A variety of the Uma language is a distinct regional or social form of Uma characterized by systematic differences in pronunciation, vocabulary, or grammar while remaining mutually intelligible with other forms.
-
E.
variety of Fang language
A variety of Fang language is a specific regional or social form of the Fang language distinguished by unique phonological, lexical, or grammatical features while remaining mutually intelligible with other Fang forms.
- F. None of above. chosen
Provenance (1 batch)
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_69e2fab772608190b74163751047ff50 |
completed | April 18, 2026, 3:29 a.m. |
Created at: April 18, 2026, 4:02 a.m.