Triple
T11793445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oluganda |
E280444
|
entity |
| Predicate | hasNativeName |
P1435
|
FINISHED |
| Object | Oluganda |
E280444
|
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: Oluganda | Statement: [Oluganda, hasNativeName, Oluganda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oluganda Context triple: [Oluganda, hasNativeName, Oluganda]
-
A.
Oluganda
chosen
Oluganda is the endonym for Luganda, a major Bantu language spoken primarily by the Baganda people in central Uganda.
-
B.
Uganda
Uganda is a landlocked country in East Africa known for its diverse landscapes, abundant wildlife, and location along the equator.
-
C.
Nzera
Nzera is a settlement located within Tanzania’s Geita Region in East Africa.
-
D.
Ogoniland
Ogoniland is an oil-rich coastal region in Nigeria’s Niger Delta, known for its Ogoni inhabitants and for severe environmental degradation linked to decades of petroleum extraction.
-
E.
Hausaland
Hausaland is a historical region of West Africa traditionally inhabited by the Hausa people, known for its influential city-states, trans-Saharan trade, and rich Islamic cultural heritage.
- 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_69d6ab258b808190b1735835c841e3a4 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a5a082d08190a42541396a06ed98 |
completed | April 10, 2026, 7:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f130fd7b9881909e79ecb49fe98d30 |
completed | April 28, 2026, 10:13 p.m. |
Created at: April 8, 2026, 9:42 p.m.