Triple
T16713422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenyan |
E406163
|
entity |
| Predicate | languageSpoken |
P151
|
FINISHED |
| Object | Kamba |
E617028
|
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: Kamba | Statement: [Kenyan, languageSpoken, Kamba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kamba Context triple: [Kenyan, languageSpoken, Kamba]
-
A.
Kamba
chosen
Kamba is a Bantu language spoken primarily by the Akamba people of Kenya, known for its rich oral traditions and regional cultural significance.
-
B.
Kambaata
Kambaata is a Cushitic language spoken primarily by the Kambaata people in southern Ethiopia.
-
C.
Dagomba
Dagomba refers to an ethnic group primarily found in northern Ghana, known for their rich cultural traditions, chieftaincy system, and use of the Dagbani language.
-
D.
Banjima
Banjima is an Aboriginal Australian people traditionally associated with the Pilbara region of Western Australia, known for their distinct language and cultural heritage.
-
E.
Kumba
Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
- 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_69d8838f242881908abd8bc138795886 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e38653cdd48190863e1cc989e21f39 |
completed | April 18, 2026, 1:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0091a95180819099abd50dd153e229 |
completed | May 10, 2026, 2:09 p.m. |
Created at: April 10, 2026, 5:20 a.m.