Triple
T12040074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ganda language |
E286635
|
entity |
| Predicate | closelyRelatedTo |
P37
|
FINISHED |
| Object | Kiga language |
E705635
|
NE 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: Kiga language | Statement: [Ganda language, closelyRelatedTo, Kiga language]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kiga language Context triple: [Ganda language, closelyRelatedTo, Kiga language]
-
A.
Kiga language
chosen
The Kiga language is a Bantu language spoken primarily by the Bakiga people of southwestern Uganda.
-
B.
Keiga language
The Keiga language is a Kadu (Kadugli) language spoken by the Keiga people in the Nuba Mountains region of Sudan.
-
C.
Keiyo language
The Keiyo language is a Southern Nilotic language spoken by the Keiyo people of Kenya’s Rift Valley, closely associated with and linguistically similar to other Kalenjin languages such as Kipsigis.
-
D.
Teke-Kega language
The Teke-Kega language is a Bantu language spoken by the Teke people of Central Africa, primarily in the Republic of the Congo and surrounding regions.
-
E.
Kawaiisu language
Kawaiisu language is an endangered Uto-Aztecan language traditionally spoken by the Kawaiisu people of southern California.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d6ab4669e48190b59246358b0383ab |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9040c1a6c8190aea1388e82dd8f5a |
completed | April 10, 2026, 2:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f649dbf081908e76c45e362217c1 |
completed | May 2, 2026, 1:04 p.m. |
Created at: April 8, 2026, 9:47 p.m.