Triple
T20354420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morogoro Region |
E496104
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Morogoro |
—
|
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: Morogoro | Statement: [Morogoro Region, containsCity, Morogoro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Morogoro Context triple: [Morogoro Region, containsCity, Morogoro]
-
A.
Morogoro
chosen
Morogoro is a major city in eastern Tanzania known as an important commercial and agricultural hub at the base of the Uluguru Mountains.
-
B.
Nyamwezi
Nyamwezi is a Bantu language spoken primarily in northwestern Tanzania by the Nyamwezi people.
-
C.
Mikocheni
Mikocheni is a residential and commercial neighborhood in Dar es Salaam, Tanzania, known for its middle-class housing, offices, and educational institutions.
-
D.
Mbeya
Mbeya is a major city in southwestern Tanzania, serving as a commercial and transport hub near the Zambian border.
-
E.
Morogoro Region
Morogoro Region is an administrative region in eastern Tanzania known for its diverse landscapes, agriculture, and proximity to major wildlife areas such as Mikumi National Park.
- 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_69e0b4a3f7f48190b37f354574028ca6 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67852ca9881908a5af18005639859 |
completed | April 20, 2026, 7:02 p.m. |
Created at: April 16, 2026, 11:25 a.m.