Triple
T10248044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mbede |
E240268
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object | Mbete |
E854829
|
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: Mbete | Statement: [Mbede, hasDialect, Mbete]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mbete Context triple: [Mbede, hasDialect, Mbete]
-
A.
Mbete-Mbede
chosen
Mbete-Mbede is a Bantu language spoken by the Mbete people primarily in parts of Gabon and the Republic of the Congo.
-
B.
Mbala
Mbala is a town in northern Zambia near the Tanzanian border, known historically as a colonial-era administrative center and for its proximity to Lake Tanganyika.
-
C.
Mbaitoli
Mbaitoli is a local government area in southeastern Nigeria known for its predominantly Igbo population and its role within Imo State’s administrative and cultural landscape.
-
D.
Mbengwi
Mbengwi is a town in western Cameroon that serves as the administrative center of Momo Division in the country's Northwest Region.
-
E.
Mtiuleti
Mtiuleti is a mountainous historical region in northeastern Georgia known for its rugged landscapes and traditional highland villages.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d22e0d4c8190a6712859924e9d3d |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d794ad73508190880a1030d483f5a8 |
completed | April 9, 2026, 11:59 a.m. |
Created at: April 6, 2026, 11:27 a.m.