Triple
T7544181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ubangian |
E178353
|
entity |
| Predicate | notableLinguaFranca |
P24056
|
FINISHED |
| Object | Sango |
—
|
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: Sango | Statement: [Ubangian, notableLinguaFranca, Sango]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableLinguaFranca Context triple: [Ubangian, notableLinguaFranca, Sango]
-
A.
notableDialect
Indicates that an entity is recognized for having a distinct or noteworthy dialect associated with it.
-
B.
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.
-
C.
isWidelySpokenIn
Indicates that a language is spoken by a large portion of the population across many regions or communities within a specified area.
-
D.
deFactoLanguage
Indicates that a language is used in practice as the primary or common language in a context, even if it has no official legal status there.
-
E.
hasLanguageOfSurroundingCountries
Indicates that an entity uses or includes the languages commonly spoken in the countries that geographically surround it.
- 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_69c69f2be3888190a6667a27f8f195e9 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f896a27481908b2e120208f268e7 |
completed | March 27, 2026, 9:37 p.m. |
| PD | Predicate disambiguation | batch_69c6f4daad6c8190af2b8ae88d2c8cb7 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:48 p.m.