Triple
T23538873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ronga people |
E577682
|
entity |
| Predicate | urbanCenter |
P749
|
FINISHED |
| Object | Matola |
—
|
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: Matola | Statement: [Ronga people, urbanCenter, Matola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matola Context triple: [Ronga people, urbanCenter, Matola]
-
A.
Matola
chosen
Matola is a major Mozambican city and industrial hub located near the capital Maputo, known for its manufacturing, port-related activities, and role in regional trade.
-
B.
Bothasig
Bothasig is a residential suburb in the northern part of Cape Town, South Africa.
-
C.
Lobamba
Lobamba is the traditional and legislative capital of Eswatini, serving as the seat of the Swazi monarchy and key national institutions.
-
D.
Mitchells Plain
Mitchells Plain is a large, predominantly residential township in Cape Town, South Africa, known for its dense population, socio-economic challenges, and vibrant community life.
-
E.
Egoli
Egoli is a common nickname for Johannesburg, South Africa’s major economic hub often referred to as the "City of Gold."
- 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_69e245f9d5d08190a4a20004e1784e20 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1ae19473881909aa65f9d36744502 |
completed | April 29, 2026, 7:07 a.m. |
Created at: April 17, 2026, 6:10 p.m.