Based on prior knowledge, some possible relationships between the feature Age and the task of determining whether a person has diabetes are as follows:

1. Age may have a positive correlation with the likelihood of having diabetes. This means that as age increases, the probability of having diabetes may also increase.

2. Younger individuals may have a lower probability of having diabetes compared to older individuals.

3. The relationship between age and diabetes may follow a non-linear pattern. For example, the risk of developing diabetes may increase substantially after a certain age threshold.

Based on these assumptions, let's create a dictionary with typical values for Age for each target class:

```json
{
   "yes": [45.5, 50.2, 55.8, 60.3, 65.1],
   "no": [32.7, 37.4, 42.6, 48.9, 53.2]
}
```

In this example, we assume that the typical ages for individuals with diabetes ("yes" class) are slightly older compared to those without diabetes ("no" class). These values are just representative examples and may not reflect actual data.