Triple
T2531857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luganda |
E56177
|
entity |
| Predicate | closelyRelatedTo |
P37
|
FINISHED |
| Object |
Lusoga
Lusoga is a Bantu language spoken primarily by the Basoga people in eastern Uganda.
|
E275006
|
NE FINISHED |
How this triple was built (4 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: Lusoga | Statement: [Luganda, closelyRelatedTo, Lusoga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lusoga Context triple: [Luganda, closelyRelatedTo, Lusoga]
-
A.
Nungua
Nungua is a coastal town and suburb of Accra in southern Ghana, known for its fishing community and vibrant local culture.
-
B.
Kasulu
Kasulu is a town in western Tanzania that serves as one of the main urban and commercial centers of the Kigoma Region.
-
C.
Wainganga
Wainganga is a major river in central India that flows through the states of Madhya Pradesh and Maharashtra before joining other rivers on its way to the Godavari basin.
-
D.
Mvita
Mvita is an alternative name for Kimvita, a historic Swahili settlement and cultural center on the coast of present-day Kenya.
-
E.
Kivu
Kivu is a conflict-affected region in eastern Democratic Republic of the Congo known for its rich natural resources, humanitarian crises, and recurrent outbreaks of violence and disease.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Lusoga Triple: [Luganda, closelyRelatedTo, Lusoga]
Generated description
Lusoga is a Bantu language spoken primarily by the Basoga people in eastern Uganda.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lusoga Target entity description: Lusoga is a Bantu language spoken primarily by the Basoga people in eastern Uganda.
-
A.
Nungua
Nungua is a coastal town and suburb of Accra in southern Ghana, known for its fishing community and vibrant local culture.
-
B.
Kasulu
Kasulu is a town in western Tanzania that serves as one of the main urban and commercial centers of the Kigoma Region.
-
C.
Wainganga
Wainganga is a major river in central India that flows through the states of Madhya Pradesh and Maharashtra before joining other rivers on its way to the Godavari basin.
-
D.
Mvita
Mvita is an alternative name for Kimvita, a historic Swahili settlement and cultural center on the coast of present-day Kenya.
-
E.
Kivu
Kivu is a conflict-affected region in eastern Democratic Republic of the Congo known for its rich natural resources, humanitarian crises, and recurrent outbreaks of violence and disease.
- F. None of above. chosen
Provenance (5 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_69ab4a48e4f081908f1218d244608659 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd2781700819091ffc32244d9efe2 |
completed | March 7, 2026, 7:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af2bb9c37081909128d7a227651c8b |
completed | March 9, 2026, 8:21 p.m. |
| NEDg | Description generation | batch_69af4fedb0a48190a9d9da8eeebfe074 |
completed | March 9, 2026, 10:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69af50551fd88190829d20ab2be426d4 |
completed | March 9, 2026, 10:57 p.m. |
Created at: March 6, 2026, 9:47 p.m.