NLU system entities

  • Release version: Zurich
  • Updated July 31, 2025
  • 5 minutes to read
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    This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.

    Summary of NLU system entities

    NLU system entities in ServiceNow enable Virtual Agent to extract structured system information from user conversations. These globally defined entities are built into NLU models by default and can be used as “nodeless” input variables in Virtual Agent topics. This helps to accurately capture key data such as dates, times, locations, people, money, and more, improving the precision and responsiveness of Virtual Agent interactions.

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    Key Entities and Their Uses

    • GLOBAL.DATE: Captures dates with various granularities including day, week, month, year, and season. Each subtype returns a normalized date string in ISO or custom formats for easy processing. For example, “Friday, February 4, 2019” normalizes to “2019-02-04”.
    • GLOBAL.TIME: Extracts time information either as specific times (hours and minutes) or parts of the day (morning, afternoon, evening, night). Example: “ten minutes to three” normalizes to “T02:50”.
    • GLOBAL.DATETIME: Combines date and time into a single normalized string accurate to minutes, e.g., “October 31st at 5:00 p.m.” becomes “2022-10-31T17:00”.
    • GLOBAL.DURATION: Identifies durations with units such as seconds, minutes, hours, days, weeks, months, or years, returning normalized values like “h48” for 48 hours.
    • GLOBAL.LOCATION: Detects location names as strings, e.g., “Santa Clara”.
    • GLOBAL.PERSON: Recognizes person names as strings, e.g., “Joe Smith”.
    • GLOBAL.MONEY: Extracts currency amounts with ISO 3166 currency codes, e.g., “USD 2000”.
    • GLOBAL.NUMBER: Identifies numeric values, e.g., “5.0”.
    • GLOBAL.SOFTWARE: Detects software names, e.g., “Java”.
    • GLOBAL.HARDWARE: Extracts hardware names, e.g., “printer”.

    Practical Benefits for ServiceNow Customers

    • Consistent Data Extraction: These predefined system entities standardize how Virtual Agent interprets common conversation elements, ensuring reliable and normalized data capture.
    • Improved Slot Filling: Entities can be used as input variables in topics, allowing Virtual Agent to automatically extract and slot-fill values from user inputs or external sources.
    • Enhanced User Experiences: By accurately understanding dates, times, locations, and other key information, Virtual Agent can provide more relevant and timely responses.
    • Out-of-the-box Availability: System entities are enabled by default in NLU models and can be managed via the NLU Workbench, simplifying setup and maintenance.

    Examples of Use

    NLU prediction results illustrate how Virtual Agent identifies entities in user utterances. For instance, when a user asks “How do I install Java?”, the GLOBAL.SOFTWARE entity extracts “Java” with a high confidence score. Similarly, “We should meet next Sunday at Starbucks” results in extraction of both a GLOBAL.DATE entity for “Sunday” and a meeting location.

    Next Steps

    ServiceNow customers can leverage these NLU system entities to build more intelligent Virtual Agent topics and scripts. Understanding the available entities and their normalized outputs helps to design conversations that efficiently gather and use critical information. For further customization and integration, review related concepts such as domain separation, Virtual Agent interaction records, and input data types.

    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
                }
            ]
        }
    ]
}