Based on prior knowledge and the given task, we can analyze the relationship between the feature NGL (Number of grains per plant) and the target variable (grain yield of soybean cultivar - low or high).

Assuming that a higher number of grains per plant indicates a greater potential for grain yield, we can expect a positive relationship between NGL and the target variable. In other words, as the number of grains per plant increases, the grain yield is more likely to be high.

To create the dictionary with typical NGL values for each target class, we can use the following approach:

1. Identify the typical range of NGL values for each target class (low and high) based on prior knowledge.
2. Select 5 values from within these ranges to represent the typical NGL values for each target class.

Here is the dictionary with the typical NGL values for the target classes 'low' and 'high':

```json
{
    "low": [22.5, 28.7, 34.2, 41.3, 47.8],
    "high": [56.2, 62.9, 71.5, 79.6, 85.2]
}
```

These values are just examples and should be replaced with appropriate ranges and specific values based on domain knowledge and data analysis.