Triple
T9671706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Lakes Bantu languages |
E234044
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object | Rutooro |
E695725
|
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: Rutooro | Statement: [Great Lakes Bantu languages, hasMember, Rutooro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rutooro Context triple: [Great Lakes Bantu languages, hasMember, Rutooro]
-
A.
Rutooro
chosen
Rutooro is a Bantu language spoken primarily by the Tooro people in western Uganda.
-
B.
Rukiga
Rukiga is a Bantu language spoken primarily by the Bakiga people in southwestern Uganda.
-
C.
Kisoro
Kisoro is a small town in southwestern Uganda known as a gateway to gorilla trekking and the nearby Bwindi Impenetrable and Mgahinga Gorilla National Parks.
-
D.
Monguno
Monguno is a town and local government area in Borno State, northeastern Nigeria, known for its strategic location and role in regional security dynamics.
-
E.
Kibondo
Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
- 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_69ca848f55e48190b3f67252571c3d45 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9c6949108190b699442e5c2aacf9 |
completed | April 1, 2026, 10:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d190f9bf78819093542adae997a668 |
completed | April 4, 2026, 10:30 p.m. |
Created at: March 30, 2026, 8:15 p.m.