To generate a temporal profile for this process, we need to calculate the average time and standard deviation between pairs of activities. However, the data provided doesn't include specific timing information for individual instances. Instead, we have frequency and total performance time for each variant.

We can estimate the average time between activities using the total performance time divided by the frequency for each variant. However, this will be a rough approximation and won't provide the level of detail required for a complete temporal profile as described in the problem statement.

Given these limitations, I'll provide a simplified temporal profile based on the available data. We'll focus on the most frequent activity pairs and use the average time for the entire variant as a proxy for the time between activities.

Here's a Python dictionary representing a simplified temporal profile:

```python
temporal_profile = {
    ('Create Fine', 'Send Fine'): (1055, 500),  # Based on the most frequent variant
    ('Send Fine', 'Insert Fine Notification'): (1055, 500),  # Assuming similar time as Create Fine to Send Fine
    ('Insert Fine Notification', 'Add penalty'): (1055, 500),  # Assuming similar time
    ('Add penalty', 'Send for Credit Collection'): (1055, 500),  # Assuming similar time
    ('Create Fine', 'Payment'): (19, 10),  # Based on the second most frequent variant
    ('Send Fine', 'Payment'): (3241, 1000),  # Based on "Create Fine -> Send Fine -> Payment" variant
    ('Add penalty', 'Payment'): (3680, 1000),  # Based on "Create Fine -> Send Fine -> Insert Fine Notification -> Add penalty -> Payment" variant
    ('Insert Date Appeal to Prefecture', 'Send Appeal to Prefecture'): (14524, 5000),  # Based on relevant variants
}
```

In this simplified temporal profile:

1. The average times are calculated by dividing the total performance time by the frequency for the relevant variants.
2. The standard deviations are rough estimates, as we don't have enough information to calculate them accurately.
3. We focus on the most frequent activity pairs and those that appear in multiple variants.

Please note that this temporal profile is a very rough approximation due to the limitations of the provided data. To create a more accurate temporal profile, we would need:

1. Individual timestamp data for each activity in each process instance.
2. The ability to calculate precise time differences between all possible pairs of activities in each trace.
3. More detailed statistical analysis to compute accurate averages and standard deviations.

For a more accurate temporal profile, you would need to analyze the raw event logs with timestamps for each activity in each process instance. This would allow for proper calculation of time differences between all activity pairs and their statistical properties.