To generate a temporal profile for a process based on the provided variants, we need to calculate the average time (performance) and the standard deviation of time intervals between the activities for each possible pair of activities in the process.

Given your input, the first step involves extracting the transitions and their associated performance times from the list of process variants. We then compute the average and standard deviation for each pair.

### Step-by-step Calculation

1. **Extract and Parse Data:**
   - For each process variant, extract the transitions.
   - Note the frequencies and performance times to compute averages.

2. **Organize Data by Pairs:**
   - For each variant, generate all possible pairs of activities and record the performance times.

3. **Compute Averages and Standard Deviations:**
   - For each pair of activities, calculate the average time (mean) and standard deviation of the performance times.

### Python Code Implementation

Here's the Python code to generate the temporal profile:

```python
import itertools
import numpy as np

# Provided process variants
variants = [
    ("Create Fine -> Send Fine -> Insert Fine Notification -> Add penalty -> Send for Credit Collection", 56482, 59591524.946),
    ("Create Fine -> Payment", 46371, 889688.400),
    ("Create Fine -> Send Fine", 20385, 8380516.026),
    ("Create Fine -> Send Fine -> Insert Fine Notification -> Add penalty -> Payment", 9520, 35037723.025),
    ("Create Fine -> Send Fine -> Insert Fine Notification -> Add penalty -> Payment -> Payment", 3736, 27487271.949),
    ("Create Fine -> Send Fine -> Insert Fine Notification -> Payment -> Add penalty -> Payment", 3301, 22266910.633),
    ("Create Fine -> Send Fine -> Payment", 3131, 10147598.595),
    ("Create Fine -> Send Fine -> Insert Fine Notification -> Insert Date Appeal to Prefecture -> Add penalty -> Send Appeal to Prefecture", 2497, 36264851.582),
    ("Create Fine -> Send Fine -> Insert Fine Notification -> Add penalty -> Payment -> Send for Credit Collection", 1515, 58289146.931),
    ("Create Fine -> Send Fine -> Insert Fine Notification -> Payment -> Add penalty -> Send for Credit Collection", 522, 54309682.759),
    ("Create Fine -> Send Fine -> Insert Fine Notification -> Add penalty -> Insert Date Appeal to Prefecture -> Send Appeal to Prefecture", 443, 39950735.892),
    ("Create Fine -> Payment -> Send Fine", 362, 5545113.812),
    ("Create Fine -> Send Fine -> Insert Fine Notification -> Insert Date Appeal to Prefecture -> Add penalty -> Send Appeal to Prefecture -> Receive Result Appeal from Prefecture -> Notify Result Appeal to Offender -> Payment", 139, 30608598.561),
    ("Create Fine -> Send Fine -> Insert Fine Notification -> Appeal to Judge -> Add penalty -> Payment", 135, 55173760.000)
    # ... More variants go here
]

def parse_variant(variant_str):
    return variant_str.split(" -> ")

data = {}

# Step 1: Extract and parse data
for variant_str, frequency, performance in variants:
    activities = parse_variant(variant_str)
    for i, j in itertools.combinations(range(len(activities)), 2):
        pair = (activities[i], activities[j])
        if pair not in data:
            data[pair] = []
        data[pair].extend([performance] * frequency)

# Step 2: Compute averages and standard deviations
temporal_profile = {}
for pair, times in data.items():
    avg_time = np.mean(times)
    std_dev = np.std(times)
    temporal_profile[pair] = (avg_time, std_dev)

# Output the temporal profile
for pair, metrics in temporal_profile.items():
    print(f"{pair}: Average Time = {metrics[0]:.3f}, Std Dev = {metrics[1]:.3f}")
```

### Explanation:
1. **Parsing Variants:** The `parse_variant` function splits a process variant string into a list of activities.
2. **Data Collection:** For each variant, all possible pairs of activities are generated using `itertools.combinations`. The corresponding performance times, scaled by frequency, are recorded.
3. **Statistics Calculation:** The average (mean) and standard deviation (`std_dev`) for the performance times of each activity pair are calculated using `numpy`.
4. **Output:** The temporal profile is printed in a readable format.

You can add the remaining variants to the `variants` list and adjust the script accordingly to handle all the data provided.

Note: Due to the extensive data, the computed average and standard deviation might require a properly scaled input, such as converting performance times to recognizable units (e.g., seconds to days).