Triple
T18074095
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lukiiko |
E432508
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Buganda |
—
|
NE NERFINISHED |
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: Buganda | Statement: [Lukiiko, locatedIn, Buganda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Buganda Context triple: [Lukiiko, locatedIn, Buganda]
-
A.
Buganda
chosen
Buganda is a traditional Bantu kingdom in central Uganda that is the country’s most populous and historically influential region.
-
B.
Nakasongola
Nakasongola is a town in central Uganda that serves as an administrative and commercial center for the surrounding rural district.
-
C.
Kanyaga
"Kanyaga" is a popular Tanzanian Bongo Flava hit song by Diamond Platnumz known for its energetic beat and danceable style.
-
D.
Bindura
Bindura is a town in northern Zimbabwe that serves as the administrative and commercial center of the surrounding mining and agricultural region.
-
E.
Bagamoyo
Bagamoyo is a historic coastal town in present-day Tanzania that served as a major 19th-century East African trade and colonial center, including as an early administrative hub for German rule.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9070cac81909fa9473fb1c3f1c7 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ccefcdc4819086d0b224731bfc4d |
completed | April 19, 2026, 12:39 p.m. |
Created at: April 10, 2026, 10:26 a.m.