NLU system entities

  • Release version: Yokohama
  • Updated January 30, 2025
  • 5 minutes to read
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    Summary of NLU System Entities

    NLU system entities in ServiceNow's Virtual Agent enable automatic extraction of key information from user conversations. These globally defined entities function as "nodeless" input variables, which can be filled by NLU predictions or provided externally. System entities are enabled by default in NLU models and are accessible via the Entities tab in the NLU Workbench.

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    Key Features

    • GLOBAL.DATE Entities: Extract dates with various granularities:
      • DAY: Specific dates (e.g., 2019-02-04)
      • WEEK: Specific week of a year (e.g., 1999W3)
      • MONTH: Specific month of a year (e.g., 1999M02)
      • YEAR: Specific year (e.g., 1999)
      • SEASON: Specific season of a year (e.g., 1999FA for Fall)
    • GLOBAL.TIME Entities: Extract time information:
      • TIME: Precise hour and minute (e.g., T02:50)
      • PARTSOFDAY: Parts of day like morning or evening (e.g., TMO)
    • GLOBAL.DATETIME Entity: Combines date and time with hour and minute precision (e.g., 2022-10-31T17:00).
    • GLOBAL.DURATION Entity: Captures durations in seconds, minutes, hours, days, weeks, months, or years (e.g., h48 for 48 hours).
    • GLOBAL.LOCATION Entity: Extracts location names as strings (e.g., Santa Clara).
    • GLOBAL.PERSON Entity: Extracts person names as strings (e.g., Joe Smith).
    • GLOBAL.MONEY Entity: Captures currency values, normalized with ISO 3166 currency codes (e.g., USD 2000).
    • GLOBAL.NUMBER Entity: Extracts numeric values (e.g., 5.0).
    • GLOBAL.SOFTWARE Entity: Recognizes software names (e.g., Java).
    • GLOBAL.HARDWARE Entity: Recognizes hardware names (e.g., printer).

    Practical Application for ServiceNow Customers

    By leveraging these NLU system entities, Virtual Agent topics can automatically identify and normalize key data from user inputs, enabling more precise and contextual responses. This reduces the need for manual input collection and improves user experience by slot-filling topic variables directly from conversation context.

    For example, when a user says "How do I install Java?" the NLU system identifies "Java" as a GLOBAL.SOFTWARE entity. Similarly, date and time inputs like "next Sunday" or "at 5:00 p.m." are recognized and normalized for processing in workflows or scripts.

    These entities come with example formats, regular expressions, and normalized value structures, which help in integrating and validating extracted data within Virtual Agent topics and workflows.

    Key Outcomes

    • Accelerates development of conversational topics by using built-in system entities.
    • Ensures consistent and accurate extraction of common entity types such as dates, times, locations, persons, money, and software/hardware.
    • Enhances Virtual Agent's ability to understand and process natural language inputs with minimal configuration.
    • Supports slot-filling for nodeless variables to streamline dialog flows and improve user interactions.

    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.

    Table 1. GLOBAL.DATE SubType = DAY usage
    Usage Example
    Format YYYY-MM-DD
    Regular expression \\d\\d\\d\\d-\\d\\d-\\d\\d
    Input example Mr. Smith left Friday, February 4, 2019.
    Normalized value 2019-02-04
    Code example 
    {
"name": "DATE", 
"value": "...",
"score": 1.0, 
"normalization": "2019-02-04"
}

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

    Table 2. GLOBAL.DATE SubType = WEEK usage
    Usage Example
    Format YYYY'W'WW
    Regular expression \\d\\d\\d\\d\\dW\\d\\d
    Input example Mr. Smith left the third week of 1999.
    Normalized value 1999W3
    Code example 
    {
"name": "entity:GLOBAL.DATE", 
"value": "...",
"score": 1.0, 
"normalization": {"type": "GLOBAL.DATE", "subType": "WEEK", "value":"1999W3"}
}

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

    Table 3. GLOBAL.DATE SubType = MONTH usage
    Usage Example
    Format YYYY'M'MM
    Regular expression \\d\\d\\d\\dM\\d\\d
    Input example Mr. Smith left in February of 1999.
    Normalized value 1999M02
    Code example 
    {
"name": "entity:GLOBAL.DATE",
"value": "...",
"score": 1.0, 
"normalization": {"type": "GLOBAL.DATE", "subType": "MONTH", "value": "1999M02"}
}

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

    Table 4. GLOBAL.DATE SubType = YEAR usage
    Usage Example
    Format YYYY
    Regular expression \\d\\d\\d\\d
    Input example Mr. Smith left in 1999.
    Normalized value 1999
    Code example 
    {
"name": "entity:GLOBAL.DATE",
"value": "...",
"score": 1.0,
"normalization": {"type": "GLOBAL.DATE", "subType": "YEAR", "value": "1999"}
}

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

    Table 5. GLOBAL.DATE SubType = SEASON usage
    Usage Example
    Format One of the following:
    • Winter: YYYYWI
    • Spring: YYYYSP
    • Summer: YYYYSU
    • Fall: YYYYFA
    Regular expression One of the following:
    • Winter: \\d\\d\\d\\dWI
    • Spring: \\d\\d\\d\\dSP
    • Summer: \\d\\d\\d\\dSU
    • Fall: \\d\\d\\d\\dFA
    Input example Mr. Smith left in the fall of 1999.
    Normalized value 1999FA
    Code example 
    {
"name": "entity:GLOBAL.DATE",
"value": "...",
"score": 1.0,
"normalization": {"type": "GLOBAL.DATE", "subType": "SEASON", "value": "1999FA"}
}

    GLOBAL.TIME system entity

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

    Table 6. GLOBAL.TIME SubType = TIME usage
    Usage Example
    Format 'T'HH:mm
    Regular expression T\\d\\d:\\d\\d
    Input example Mr. Smith left at ten minutes to three.
    Normalized value T02:50
    Code example 
    {
"name": "entity:GLOBAL.TIME", 
"value": "...",
"score": 1.0, 
"normalization": {"type": "GLOBAL.TIME","subType": "TIME", "value": "T02:50"}
}

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

    Table 7. GLOBAL.TIME SubType = PARTSOFDAY usage
    Usage Example
    Format One of the following:
    • Morning: TMO
    • Afternoon: TAF
    • Evening: TEV
    • Night: TNI
    Regular expression One of the following:
    • Morning: TMO
    • Afternoon: TAF
    • Evening: TEV
    • Night: TNI
    Input example Mr. Smith left in the morning.
    Normalized value TMO
    Code example 
    {
"name": "entity:GLOBAL.TIME", 
"value": "...",
"score": 1.0, 
"normalization": {"type": "GLOBAL.TIME", "subType": "PARTSOFDAY", "value": "TMO"}
}

    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.

    Table 8. GLOBAL.DATE_TIME SubType = DATETIME usage
    Usage Example
    Format YYYY-MM-DD'T'HH:mm
    Regular expression \\d\\d\\d\\d-\\d\\d-\\d\\dT\\d\\d:\\d\\d
    Input example Mr. Smith leaves on October 31st at 5:00 p.m.
    Normalized value 2022-10-31T17:00
    Code example 
    {
"name": "DATE_TIME",
"value": "October 31st at 5:00 p.m",
"normalization": "2022-10-31T17:00","confidence": "1"
}

    GLOBAL.DURATION system entity

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

    Table 9. GLOBAL.DURATION usage
    Usage Example
    Format One of the following:
    • Second: 's'ss
    • Minute: 'm'mm
    • Hour: 'h'hh
    • Day: 'D'DD
    • Week: 'W'WW
    • Month: 'M'MM
    • Year: 'Y'YY
    Regular expression One of the following:
    • Second: s\\d\\d
    • Minute: m\\d\\d
    • Hour: h\\d\\d
    • Day: D\\d\\d
    • Week: W\\d\\d
    • Month: M\\d\\d
    • Year: Y\\d\\d
    Input example Mr. Smith stayed in Boston for 48 hours.
    Normalized value h48
    Code example 
    {
"name": "entity:GLOBAL.DURATION",
"value": "...", 
"score": 1.0,
"normalization": {"type": "GLOBAL.DURATION", "value": "h48"}
}

    GLOBAL.LOCATION system entity

    This entity returns a location string.

    Table 10. GLOBAL.LOCATION usage
    Usage Example
    Format String value. Example: Santa Clara
    Regular expression Not applicable.
    Input example Mr. Smith works in Santa Clara.
    Normalized value Santa Clara
    Code example 
    {
"name": "entity:GLOBAL.LOCATION",
"value": "...", 
"score": 1.0,
"normalization": {"type": "GLOBAL.LOCATION", "value":"Santa Clara"}
}

    GLOBAL.PERSON system entity

    This entity returns a name string.

    Usage Example
    Format String value. Example: Joe Smith
    Regular expression Not applicable.
    Input example Joe Smith works in Santa Clara.
    Normalized value Joe Smith
    Code example 
    {
"name": "entity:GLOBAL.PERSON", 
"value": "...",
"score": 1.0, 
"normalization": {"type": "GLOBAL.PERSON", "value":"Joe Smith"}
}

    GLOBAL.MONEY system entity

    This entity returns a currency string.

    Table 11. GLOBAL.MONEY usage
    Usage Example
    Format String value. Example: USD 2000
    Regular expression Not applicable.
    Input example Show me laptops for less than $2000.
    Normalized value USD 2000
    Note:
    The normalized value uses the three-letter ISO 3166 country code of the source currency.
    Code example 
    {
"name": "entity:GLOBAL.MONEY", 
"value": "...",
"score": 1.0, 
"normalization": {"type": "GLOBAL.MONEY", "value":"2000", “currency”:”USD”}
}

    GLOBAL.NUMBER system entity

    This entity returns a number.

    Usage Example
    Format String value. Example: 5.0
    Regular expression Not applicable.
    Input example I want to see the previous 5 transactions from my account.
    Normalized value 5.0
    Code example 
    {
"name": "entity:GLOBAL.NUMBER",
  "value": "...",
  "score": 1.0,
  "normalization": {"numericValue":"5", “normalizedValue”: “5”}
}

    GLOBAL.SOFTWARE

    Returns a software string.

    Usage Example
    Format String value. Example: Java
    Regular expression Not applicable.
    Input example How do I install Java?
    Normalized value Java
    Code example 
    {
  "name": "entity:GLOBAL.SOFTWARE",
  "value": "Java",
  "score": 0.99930537,
  "normalization": {"type":"entity:GLOBAL.SOFTWARE",
                     "subType":"SOFTWARE",
                     "value":"Java"}
}

    GLOBAL.HARDWARE

    Returns a hardware string.

    Usage Example
    Format String value. Example: printer
    Regular expression Not applicable.
    Input example How do I order a printer?
    Normalized value printer
    Code example 
    {
  "name": "entity:GLOBAL.HARDWARE",
  "value": "printer",
  "score": 1.0,
  "normalization": {"type":"entity:GLOBAL.HARDWARE",
                     "subType":"HARDWARE",
                     "value":"printer"}
}

    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
                }
            ]
        }
    ]
}