Triple
T11123442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mbare Brewery |
E263073
|
entity |
| Predicate | neighbourhood |
P988
|
FINISHED |
| Object | Mbare township |
E48970
|
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: Mbare township | Statement: [Mbare Brewery, neighbourhood, Mbare township]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mbare township Context triple: [Mbare Brewery, neighbourhood, Mbare township]
-
A.
Mbare
chosen
Mbare is one of the oldest and most densely populated townships in Harare, Zimbabwe, known as a major transport hub and bustling market area.
-
B.
Mathare
Mathare is a densely populated informal settlement and neighborhood in Nairobi, Kenya, known for its extensive slums and socio-economic challenges.
-
C.
Orangi Town
Orangi Town is a densely populated residential area in Karachi, Pakistan, known as one of Asia’s largest informal settlements.
-
D.
Kadoma
Kadoma is a city in central Zimbabwe known for its gold mining and agricultural activities.
-
E.
Kadoma
Kadoma is a city in Osaka Prefecture, Japan, known as a residential and commercial suburb within the Osaka metropolitan area.
- 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_69d6aa9b46cc8190b19f9f0cc45bf322 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7e82e933481908550499cf9dd6531 |
completed | April 9, 2026, 5:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e496b4cadc8190b82ac12061c31bdc |
completed | April 19, 2026, 8:47 a.m. |
Created at: April 8, 2026, 9:28 p.m.