Absolutely! Here's a temporal profile for a hypothetical Purchase-to-Pay process in Python dictionary format:

```python
temporal_profile = {
  ('Requisition Submitted', 'Purchase Order Created'): (2 * 3600, 7200),  # 2 hours avg, 2 hours std. dev. (can vary)
  ('Purchase Order Created', 'Vendor Quote Received'): (5 * 3600, 18000), # 5 hours avg, 5 hours std. dev. (can vary based on complexity)
  ('Vendor Quote Received', 'Purchase Order Approved'): (1 * 86400, 43200), # 1 day avg, 12 hours std. dev. (approval process can take time)
  ('Purchase Order Approved', 'Goods Received'): (7 * 86400, 2 * 86400), # 7 days avg, 2 days std. dev. (depends on shipping)
  ('Goods Received', 'Invoice Received'): (3 * 86400, 1 * 86400), # 3 days avg, 1 day std. dev. (receiving may precede invoice)
  ('Invoice Received', 'Payment Issued'): (5 * 86400, 2 * 86400), # 5 days avg, 2 days std. dev. (accounting processing)
}
```

**Explanation:**

* Each key in the dictionary is a tuple representing a pair of consecutive activities in the Purchase-to-Pay process.
* The value for each key is another tuple containing two elements:
    * The first element represents the average time (in seconds) between the two activities.
    * The second element represents the standard deviation (in seconds) of the time between the activities.

**Notes:**

* These are hypothetical values and can vary depending on your specific organization and industry. 
* You can adjust the averages and standard deviations based on your historical data or best estimates.
* This is a simplified example, and additional activities might be present in a real Purchase-to-Pay process, such as three-way matching (comparing invoice, purchase order, and receiving report).

This temporal profile allows you to identify potential deviations in your Purchase-to-Pay process by comparing actual execution times with the expected averages and standard deviations. 