Based on prior knowledge, let me analyze the relationship between the feature "Glucose" and the task of determining whether a person has diabetes or not.

In general, high glucose levels in the blood can indicate diabetes. However, it is important to consider that glucose levels can fluctuate throughout the day and can be influenced by various factors such as diet, physical activity, and stress.

To determine the appropriate ranges of values, we can consider the normal range for fasting plasma glucose levels, which is typically between 70 and 99 mg/dL. However, for an oral glucose tolerance test (OGTT), higher glucose values are expected as the person is given a high-sugar drink to drink quickly. In this case, a range between 140 and 199 mg/dL two hours after the glucose intake can indicate impaired glucose tolerance, which is a precursor to diabetes. Values above 200 mg/dL on an OGTT are generally considered diagnostic for diabetes.

Based on this analysis, let's create the dictionary with the required format:

```json
{
	"yes": [200.5, 210.2, 220.0, 250.1, 280.6],
	"no": [90.2, 85.9, 95.1, 92.7, 89.5]
}
```

This dictionary provides five typical values of the feature "Glucose" for each target class ('yes' and 'no'). The values presented are floats representing the glucose levels. Please note that these values are just examples and may not reflect the exact relationship between the feature and the target in a specific dataset.