Triple
T7603505
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Igunga District |
E180042
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Igunga |
E182641
|
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: Igunga | Statement: [Igunga District, capital, Igunga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Igunga Context triple: [Igunga District, capital, Igunga]
-
A.
Igunga
chosen
Igunga is a town and district in central Tanzania known for its agricultural activities, particularly cotton and livestock farming, within the Tabora Region.
-
B.
Luyengo
Luyengo is a locality in Eswatini known for hosting the Luyengo Campus of the University of Eswatini and its agricultural education facilities.
-
C.
Mungaka
Mungaka is a Grassfields Bantu language spoken primarily in Cameroon, particularly associated with the Bamunka (Ndop) area.
-
D.
Kibondo
Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
-
E.
Oshikwanyama
Oshikwanyama is a Bantu language variety spoken primarily in northern Namibia and southern Angola, recognized as one of the major dialects of Oshiwambo.
- 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_69c69f3567008190ab01d2ca7b53584a |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9fa633081909660f653f5b073cd |
completed | March 27, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8ac8a4e2c81909b8038b2da8e806d |
completed | March 29, 2026, 4:37 a.m. |
Created at: March 27, 2026, 3:54 p.m.