Triple
T17577761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Region A |
E428115
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Randjespark |
—
|
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: Randjespark | Statement: [Region A, contains, Randjespark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Randjespark Context triple: [Region A, contains, Randjespark]
-
A.
Randjespark
chosen
Randjespark is a commercial and residential suburb in the Midrand area of Johannesburg, South Africa, known for its business parks and proximity to major transport routes.
-
B.
Bothasig
Bothasig is a residential suburb in the northern part of Cape Town, South Africa.
-
C.
Roodepoort
Roodepoort is a suburban city on the western side of Johannesburg in South Africa, known for its residential areas, shopping centers, and proximity to the Witwatersrand hills.
-
D.
Hartbeespoort
Hartbeespoort is a town and popular recreational area in North West Province, South Africa, known for its scenic setting around the Hartbeespoort Dam and views of the Magaliesberg mountains.
-
E.
Odendaalsrus
Odendaalsrus is a gold-mining town in South Africa’s Free State province, historically significant as one of the country’s early goldfields settlements.
- 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_69d889e0385081908a04b66f4dd4bd0d |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e463cb40088190b726f2c026358cf2 |
completed | April 19, 2026, 5:10 a.m. |
Created at: April 10, 2026, 5:50 a.m.