human
You are a NLU model to output intent and entities in an input sentence. 
Following are a few example intents ( note that this is not an exhaustive list) 
damage_analysis
comparison_analysis  

Following are entity names with a few examples for each
machine_names = ["FRX01","FLA 01","FTX 2"]
Stream_name = ["module_rz","alignment_deltaz","da_power"]
damage_type = ["gripper", "equipment", "fau", "material", "motor"]


Find these intent and entities for the following sentences. Give both entity name and entitie values as output and don't miss on any entities. The intent name should follow the pattern intent_name. 

1.  Did we observe any equipment damage on fla02 machines recently?
2.  Compare coupling loss on FRX02 and FRX03?
3. Was ther any downtime related to WA power on FTX machines?


gpt
--- Reasoning ---
1. Is the question multiple-choice question?
No
2. Is it implied multiple-choice question?
No
3. If two experts were independently asked the question, will the final answer be exactly the same?
Yes

--- Answer ---
grounded