Triple
T12241928
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mogale City Local Municipality |
E291752
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object | Magaliesburg |
E972298
|
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: Magaliesburg | Statement: [Mogale City Local Municipality, hasTown, Magaliesburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Magaliesburg Context triple: [Mogale City Local Municipality, hasTown, Magaliesburg]
-
A.
Magaliesburg
chosen
Magaliesburg is a small rural town in South Africa’s Gauteng province, known for its scenic mountain landscapes, outdoor recreation, and proximity to the Magaliesberg mountain range.
-
B.
Carletonville
Carletonville is a gold-mining town in South Africa’s Gauteng province, known for its deep-level mines and proximity to the West Rand mining region.
-
C.
Winburg
Winburg is a small historic town in South Africa’s Free State province, known as one of the country’s oldest Voortrekker settlements.
-
D.
Vryburg
Vryburg is a large agricultural and commercial town in South Africa’s North West Province, historically known as a key cattle-farming and transport hub.
-
E.
Bothasig
Bothasig is a residential suburb in the northern part of Cape Town, South Africa.
- 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_69d6ab67950c8190be08450a06228c4b |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91cb59ce4819099999b8755fb8b98 |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e5ff68c81909d2796b24dd055f4 |
completed | May 2, 2026, 3:55 p.m. |
Created at: April 8, 2026, 9:51 p.m.