Triple
T21153512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zanzibar Channel |
E521252
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Bagamoyo |
—
|
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: Bagamoyo | Statement: [Zanzibar Channel, near, Bagamoyo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bagamoyo Context triple: [Zanzibar Channel, near, Bagamoyo]
-
A.
Bagamoyo
chosen
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.
-
B.
Karume
Karume is a Swahili surname most prominently associated with Abeid Karume, the first president of Zanzibar and a key figure in Tanzanian political history.
-
C.
Mungaka
Mungaka is a Grassfields Bantu language spoken primarily in Cameroon, particularly associated with the Bamunka (Ndop) area.
-
D.
Ngamo
Ngamo is a West Chadic language spoken primarily in northeastern Nigeria by the Ngamo people.
-
E.
Kibondo
Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
- 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_69e0b50d1ea481909c07e63c3ead9316 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7252929748190afd85be40294293f |
completed | April 21, 2026, 7:20 a.m. |
Created at: April 16, 2026, 2:58 p.m.