Triple
T7911490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mbeya Region |
E183708
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | Mbeya |
E637653
|
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: Mbeya | Statement: [Mbeya Region, largestCity, Mbeya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mbeya Context triple: [Mbeya Region, largestCity, Mbeya]
-
A.
Mbeya
chosen
Mbeya is a major city in southwestern Tanzania, serving as a commercial and transport hub near the Zambian border.
-
B.
Masindi
Masindi is a town in western Uganda that serves as a key gateway and service center for visitors to Murchison Falls National Park.
-
C.
Mbabane
Mbabane is the largest city and administrative center of Eswatini, located in the country's western highlands.
-
D.
Zomba
Zomba is a historic city in southern Malawi that served as the country’s former capital and remains an important administrative and educational center.
-
E.
Nyamwezi
Nyamwezi is a Bantu language spoken primarily in northwestern Tanzania by the Nyamwezi people.
- 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_69ca828dec0c81908b8f55a4dbbb53ff |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a725b8c8190a530adb3107a95dd |
completed | March 31, 2026, 3:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc936b4d088190bfcfd3bc6c05f7e8 |
completed | April 1, 2026, 3:39 a.m. |
Created at: March 30, 2026, 5:04 p.m.