Let's carefully analyze the JSON output from the exported LUIS (Language Understanding) model.
The utterance is:
" average amount of rain by month at chicago last year "
The extracted entities are:
Weather.WeatherRange # " average " (positions 0-6)
Weather.WeatherCondition # " rain " (positions 18-21)
Weather.Historic # " by month " (positions 23-30)
We need to determine what text fragment corresponds to Weather.Historic .
Looking at the " startPos " : 23, " endPos " : 30 , these indexes map to the substring " by month " in the utterance.
Why not the other options?
B). chicago # Refers to a location entity, but the entity name is Weather.Location (not shown here).
C). rain # Already captured as Weather.WeatherCondition .
D). location # Would also be "chicago," but again that's a different entity type.
So the only correct match for Weather.Historic is " by month " .
The answer: A. by month
Microsoft References
Entities in LUIS
Export LUIS model schema
Labeling utterances with entities