Triple
T483530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sudanese Arabic |
E9822
|
entity |
| Predicate | standardLanguageOf |
P13499
|
FINISHED |
| Object | no country (vernacular only) |
—
|
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: no country (vernacular only) | Statement: [Sudanese Arabic, standardLanguageOf, no country (vernacular only)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: standardLanguageOf Context triple: [Sudanese Arabic, standardLanguageOf, no country (vernacular only)]
-
A.
officialLanguage
Indicates that a particular language has been formally designated by an authority as the official language used for government, legal, or administrative purposes in a given jurisdiction.
-
B.
primaryLanguageOf
Indicates that a specified language is the main or official language used by a particular entity (such as a person, organization, or region).
-
C.
majorityLanguageOf
Indicates that a given language is the primary or most widely spoken language within a specified group, region, or entity.
-
D.
regionLanguage
Indicates that a particular language is used or officially recognized within a specific geographic region.
-
E.
isLanguageOf
Indicates that a particular language is used as the official or primary language associated with a given entity (such as a person, document, or region).
- F. None of above. chosen
Provenance (4 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_69a2e802e2908190ab17c9479e0b6412 |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f0b8fe6081909f8ab87bfda6b2d8 |
completed | Feb. 28, 2026, 1:42 p.m. |
| PD | Predicate disambiguation | batch_69a2edf48ec08190b85d07e194f99c49 |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2eeba8a488190986cc7381332f783 |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.