Triple
T36631372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sara–Bagirmi languages |
E904325
|
entity |
| Predicate | areMutuallyIntelligibleToSomeExtent |
P7448
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Sara–Bagirmi languages, areMutuallyIntelligibleToSomeExtent, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areMutuallyIntelligibleToSomeExtent Context triple: [Sara–Bagirmi languages, areMutuallyIntelligibleToSomeExtent, true]
-
A.
areMutuallyIntelligibleToSomeDegree
chosen
Indicates that two or more languages or communication systems can be at least partially understood by each other’s users without prior learning or translation.
-
B.
lessMutuallyIntelligibleThan
Indicates that the level of mutual intelligibility between one pair of languages (or language varieties) is lower than that between another pair.
-
C.
areMutuallyIntelligibleWithSerer
Indicates that two languages can be understood by Serer speakers and vice versa without prior specialized study.
-
D.
coversMutuallyUnintelligibleVarieties
Indicates that the relationship involves a linguistic variety encompassing two or more other varieties whose speakers cannot understand each other.
-
E.
hasLanguageSimilarTo
Indicates that one entity uses or is associated with a language that is similar or closely related to the language used or associated with 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_69f76e6c63e48190b1d0c3a79a6c7406 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f7c777e924819081a6634f549fe552 |
completed | May 3, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69f7c477a4d481908f52e55b6688f60c |
completed | May 3, 2026, 9:56 p.m. |
Created at: May 3, 2026, 4:11 p.m.