Triple
T19672480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tanzam Highway |
E472366
|
entity |
| Predicate | connectsTo |
P845
|
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: [Tanzam Highway, connectsTo, Morogoro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Morogoro Context triple: [Tanzam Highway, connectsTo, Morogoro]
-
A.
Nyamwezi
Nyamwezi is a Bantu language spoken primarily in northwestern Tanzania by the Nyamwezi people.
-
B.
Mikocheni
Mikocheni is a residential and commercial neighborhood in Dar es Salaam, Tanzania, known for its middle-class housing, offices, and educational institutions.
-
C.
Mbeya
Mbeya is a major city in southwestern Tanzania, serving as a commercial and transport hub near the Zambian border.
-
D.
Morogoro Region
chosen
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.
-
E.
Likasi
Likasi is a mining city in the southeastern Democratic Republic of the Congo, known for its significant copper and cobalt production.
- 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_69d8e514f2e08190ba70a4449519d218 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6416d61008190af531c6d346d7da1 |
completed | April 20, 2026, 3:08 p.m. |
Created at: April 10, 2026, 1:45 p.m.