Triple
T2031959
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Entebbe |
E44536
|
entity |
| Predicate | languageUsed |
P238
|
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: [Entebbe, languageUsed, Luganda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luganda Context triple: [Entebbe, languageUsed, 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.
Nyamwezi language
The Nyamwezi language is a Bantu language spoken primarily by the Nyamwezi people of western-central Tanzania.
-
C.
Kimbundu
Kimbundu is a major Bantu language spoken primarily in northwestern Angola, especially around the capital Luanda, by the Ambundu people.
-
D.
Kikongo
Kikongo is a Bantu language widely spoken in Central Africa, particularly in the western regions of the Democratic Republic of the Congo and neighboring countries.
-
E.
Chichewa
Chichewa is a major Bantu language spoken primarily in Malawi and neighboring countries, serving as a national and widely used lingua franca in the 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_69a889144f2481909932f0746a93023d |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9313134819088133fb69b8f606f |
completed | March 7, 2026, 5:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae0b00d0a48190bcd1924df2786e41 |
completed | March 8, 2026, 11:49 p.m. |
Created at: March 4, 2026, 7:38 p.m.