Triple
T31768202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | East African English |
E810864
|
entity |
| Predicate | regionallyVaries |
P32680
|
FINISHED |
| Object | urban East African English |
—
|
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: urban East African English | Statement: [East African English, regionallyVaries, urban East African English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionallyVaries Context triple: [East African English, regionallyVaries, urban East African English]
-
A.
rateVariesBy
Indicates that the rate of something changes depending on a specified factor, condition, or category.
-
B.
regionCountVariesBy
Indicates that the number of regions associated with an entity changes depending on another specified factor or condition.
-
C.
hasRegionalVariationsIn
chosen
Indicates that something exhibits different forms, versions, or characteristics depending on the geographic region.
-
D.
termVariesBy
Indicates that the value or meaning of a term changes depending on a specified factor, such as context, dimension, or condition.
-
E.
regionallyDistinctFrom
Indicates that two entities differ from each other in characteristics or classification based on their geographic or regional context.
- 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_69f348e463e08190b902d4819195e1f0 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fea2d0a4d08190aa06aeb902a02d5a |
completed | May 9, 2026, 2:58 a.m. |
| PD | Predicate disambiguation | batch_69fea24698348190b9b992a8e7cdbcd0 |
completed | May 9, 2026, 2:56 a.m. |
Created at: April 30, 2026, 11:33 p.m.