Based on the process variants provided, we can identify some transitions between activities. However, specifying the exact average times and standard deviations will depend on the detailed durations of each activity in each process instance executed. Since we don't have these values, we'll create a hypothetical temporal profile which represents an average behavior of a process following the principle you've described.

A temporal profile for a process with these activities would be something like:

**Hypothetical Temporal Profile:**

```
{ ('A', 'B'): (avg_time_A_to_B, std_dev_A_to_B), ... }
```

Assuming an average time of `1 day` (86400 seconds) for a process transition and a standard deviation of `1 hour` (3600 seconds), we can create profiles for transitions that are mentioned based on common business understanding or simplifying assumptions. Heres a simplified version for some transitions:

```
{
    # From here you would list possible (A, B) transitions and their averages/SDs
    ('Create Fine', 'Send Fine'): (72000, 1800),  # An hour average, assuming that A to B takes roughly this duration considering potential synchronous or asynchronous operations
    ('Send Fine', 'Insert Fine Notification'): (1800, 360),  # Convert the proportionate timechange based on manually assessed durations or existence of delays
    ('Insert Fine Notification', 'Add penalty'): (1200, 240),  # Time to add penalty subsequent to notification
    ...

    # Additional entries might be calculated or estimated based on data specific to the organization or process context
}
```

This example assumes that there are direct dependencies between activities, which translates into a simple (A, B) temporal relationship. In a real-world process, additional factors such as concurrency between different transitions, resource usage, and business logic could impact the actual averages and standard deviations.

To create an accurate temporal profile, you'd need duration data corresponding to each specific tuple of activities (A, B). This data would come from a process mining toolkit like ProM or by analyzing execution logs to track the start and end times of activities, thus calculating the durations between transitions.

Please note that this is an illustrative approach without specific data, and the actual temporal profile would require detailed data analysis and process understanding. It's also important to consider that the quality of the temporal profile can be affected by the completeness and quality of the process