Use globally defined NLU entities to identify system information that Virtual Agent can extract from the conversation. You can define entities as "nodeless" input variables for a topic. These variables can be slot-filled from NLU service provider predictions or provided outside of the scope of the topic.

System entities are enabled in NLU models by default. You can view them on the model Entities tab in NLU Workbench.

GLOBAL.DATE system entity

The DAY SubType returns a date string that is accurate to a specific date.

The WEEK SubType returns a date string of a specific week of a year.

The MONTH SubType returns a date string of a specific month of a year.

The YEAR SubType returns a date string of a specific year.

The SEASON SubType returns a date string of a specific season of the year.

GLOBAL.TIME system entity

The TIME SubType returns a time string that is accurate to an hour and a minute.

The PARTSOFDAY SubType returns a time string that specifies parts of the day.

GLOBAL.DATE_TIME system entity

The DATE_TIME SubType returns a date string that is accurate to a specific date and time string that is accurate to an hour and a minute.

GLOBAL.DURATION system entity

This entity returns a duration string that specifies the duration of the activity.

GLOBAL.LOCATION system entity

This entity returns a location string.

GLOBAL.PERSON system entity

This entity returns a name string.

GLOBAL.MONEY system entity

This entity returns a currency string.

GLOBAL.NUMBER system entity

This entity returns a number.

GLOBAL.SOFTWARE

Returns a software string.

GLOBAL.HARDWARE

Returns a hardware string.

Example NLU prediction result using Software system entity

{"status":"success",
   "response":{
      "utterance":"How do I install Java?",
      "intents":[
         {
            "intentName":"test intent",
            "nluModelName":"ml_x_snc_global_global_268a97a9dbd23c107906265d1396191a",
            "score":0.90401393,
            "intents":[
               
            ],
            "entities":[
               {
                  "name":"entity:GLOBAL.SOFTWARE",
                  "value":"Java",
                  "score":0.99930537,
                  "normalization":{
                     "type":"entity:GLOBAL.SOFTWARE",
                     "subType":"SOFTWARE",
                     "value":"Java"
                  },
                  "startingPosition":-1
               }
            ]
         }
      ],
      "properties":{
         "all:ml_x_snc_global_global_268a97a9dbd23c107906265d1396191a":"0.55",
         "entity:all":"0.01",
         "inference.sspace.time":"4",
         "inference.time":"33",
         "intent:all":"0.01",
         "nluPlatformLanguage":"en",
         "nluPlatformVersion":"rome.0"
      }
   }
}

Example NLU prediction result using DATE system entity

{
    "utterance": "We should meet next Sunday at Starbucks.",
    "intents": [
        {
            "intentName": "intent:Desire.Desire",
            "score": 0.83452,
            "entities": []
        },
        {
            "intentName": "intent:Meeting.MeetRequest",
            "score": 0.8919042,
            "entities": [
                {
                    "entityName": "entity:Meeting.MeetRequest.Where",
                    "value": "Starbucks",
                    "score": 1
                },
     {
                    "entityName": "entity:GLOBAL.DATE",
                    "value": "Sunday",
                    "normalization": { "type": "DATE",
                        "subType": "DAY",
                        "value": "1999-10-01"
                     },
                    "score": 0.87
                }
            ]
        }
    ]
}