Triple
T18656686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mafinga Region |
E456082
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | Mafinga Region |
—
|
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: Mafinga Region | Statement: [Mafinga Region, hasName, Mafinga Region]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mafinga Region Context triple: [Mafinga Region, hasName, Mafinga Region]
-
A.
Mafinga region
chosen
Mafinga Region is an administrative area in Tanzania that includes Mafinga Central and surrounding localities.
-
B.
Mwanza Region
Mwanza Region is an administrative region in northwestern Tanzania, located along the southern shores of Lake Victoria and known as a major economic and cultural center, including for the Sukuma people.
-
C.
Ruvuma Region
Ruvuma Region is a largely rural administrative area in southern Tanzania known for its wildlife, forests, and proximity to major conservation areas.
-
D.
Vumba region
The Vumba region is a scenic highland area in eastern Zimbabwe known for its lush forests, cool misty climate, and rich biodiversity, attracting nature lovers and tourists.
-
E.
Negombo region
The Negombo region is a coastal area in western Sri Lanka known for its fishing industry, beaches, and proximity to the country’s main international airport.
- 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_69d8d38ea1e88190997e9b231190ba6f |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e55084ca3481909ff3fd9045f25dcd |
completed | April 19, 2026, 10 p.m. |
Created at: April 10, 2026, 11:47 a.m.