Triple
T10568186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mathare River |
E249405
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Mathare |
E216465
|
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: Mathare | Statement: [Mathare River, flowsThrough, Mathare]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mathare Context triple: [Mathare River, flowsThrough, Mathare]
-
A.
Mathare
chosen
Mathare is a densely populated informal settlement and neighborhood in Nairobi, Kenya, known for its extensive slums and socio-economic challenges.
-
B.
Kibera
Kibera is one of Africa’s largest informal settlements, located in Nairobi, Kenya, known for its dense population, poverty, and vibrant community life.
-
C.
Mbare
Mbare is one of the oldest and most densely populated townships in Harare, Zimbabwe, known as a major transport hub and bustling market area.
-
D.
Nairobi West
Nairobi West is a residential and commercial neighborhood in Nairobi, Kenya, known for its proximity to the city center and mixed middle-income housing.
-
E.
Kadoma
Kadoma is a city in central Zimbabwe known for its gold mining and agricultural activities.
- 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5272ff53c8190ae7c399d49b585f5 |
completed | April 7, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d94b4c26ec8190910efdf4a236d654 |
completed | April 10, 2026, 7:11 p.m. |
Created at: April 6, 2026, 12:37 p.m.