Triple
T35911802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Randfontein Local Municipality |
E1038633
|
entity |
| Predicate | formedRandWestCityWith |
P202390
|
FINISHED |
| Object | Westonaria Local Municipality |
—
|
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: Westonaria Local Municipality | Statement: [Randfontein Local Municipality, formedRandWestCityWith, Westonaria Local Municipality]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: formedRandWestCityWith Context triple: [Randfontein Local Municipality, formedRandWestCityWith, Westonaria Local Municipality]
-
A.
protagonistCity
Indicates the city in which the main character or protagonist of a work is primarily based or associated.
-
B.
workCity
Indicates the city in which an entity (typically a person) performs their work or job.
-
C.
builtCityWhere
Indicates that an agent constructed or established a city at the specified location.
-
D.
nativeCity
Indicates that a city is the place where a person was born or is originally from.
-
E.
city1
Indicates that the subject is classified as a city.
- F. None of above. chosen
Provenance (4 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_69f76e2259608190bf6788a132e0d139 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a007899cadc8190a04edd503eaf6514 |
completed | May 10, 2026, 12:22 p.m. |
| PD | Predicate disambiguation | batch_6a0078493e088190b0c5047cbe75d304 |
completed | May 10, 2026, 12:21 p.m. |
| PDg | Predicate description generation | batch_6a00789927708190b031d3a9d5f4f68e |
completed | May 10, 2026, 12:22 p.m. |
Created at: May 3, 2026, 4:07 p.m.