Triple
T17107030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Kato |
E415126
|
entity |
| Predicate | hasLanguage |
P15
|
FINISHED |
| Object | Luganda |
E56177
|
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: Luganda | Statement: [David Kato, hasLanguage, Luganda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luganda Context triple: [David Kato, hasLanguage, Luganda]
-
A.
Luganda
chosen
Luganda is a major Bantu language spoken primarily in Uganda, serving as a key lingua franca and cultural language of the Baganda people.
-
B.
Kitwe
Kitwe is a major mining and industrial city in Zambia’s Copperbelt Province, known as one of the country’s largest urban and economic centers.
-
C.
Tumbuka
Tumbuka is a Bantu language spoken primarily in northern Malawi and parts of Zambia and Tanzania.
-
D.
Kinyankole language
The Kinyankole language is a Bantu language spoken primarily by the Banyankole people in southwestern Uganda.
-
E.
Sukuma–Nyamwezi languages
The Sukuma–Nyamwezi languages are a closely related group of Bantu languages spoken primarily in northwestern Tanzania by the Sukuma, Nyamwezi, and neighboring ethnic groups.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dc280b0c8190b9e620b90e0d4b40 |
completed | April 18, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a013a019540819083ce6100b24f8cfb |
completed | May 11, 2026, 2:08 a.m. |
Created at: April 10, 2026, 5:35 a.m.