Triple
T24921570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tafi Abuife |
E618740
|
entity |
| Predicate | regionalLinguaFranca |
P24056
|
FINISHED |
| Object | Ewe |
—
|
NE NERFINISHED |
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: Ewe | Statement: [Tafi Abuife, regionalLinguaFranca, Ewe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionalLinguaFranca Context triple: [Tafi Abuife, regionalLinguaFranca, Ewe]
-
A.
nationalLinguaFranca
Indicates that a language functions as the primary common means of communication across different linguistic groups within a nation.
-
B.
regionLanguage
Indicates that a particular language is used or officially recognized within a specific geographic region.
-
C.
isFrancophoneCounterpartOf
Indicates that one entity serves as the French-speaking or French-language equivalent or counterpart of another entity.
-
D.
isLinguaFrancaOf
chosen
Indicates that a language serves as a common medium of communication between speakers of different native languages within a particular region, community, or context.
-
E.
regionalDialect
Indicates that one entity uses or is associated with a dialect specific to a particular geographic region in relation to another entity.
- 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_69e2fab9edd88190b86004a78a28bc20 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f6430a93a48190854ce71df680b2fa |
completed | May 2, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69f641da05b881909f6283c988639c53 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 18, 2026, 5:28 a.m.