NLU system entities

Washington DC ServiceNow AI Platform Capabilities

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Washington DC ServiceNow AI Platform Capabilities

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NLU system entities

Release version: Washingtondc

Updated February 1, 2024

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Summary of NLU System Entities Extend ServiceNow AI Platform Capabilities

The NLU system entities within ServiceNow's AI Platform enhance the Virtual Agent’s ability to extract pertinent information from conversations. These globally defined entities allow for "nodeless" input variables that can be filled using predictions from NLU service providers or provided externally. System entities are enabled by default in NLU models and can be accessed via the Entities tab in the NLU Workbench.

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Key Features

GLOBAL.DATE: Identifies specific dates with subtypes including DAY, WEEK, MONTH, YEAR, and SEASON. Each subtype has a defined format and example usage to facilitate accurate date extraction.
GLOBAL.TIME: Captures specific times of the day, with subtypes like TIME and PARTSOFDAY, providing formats for precise time extraction.
GLOBAL.DATETIME: Combines date and time into a single string, allowing for detailed temporal information.
GLOBAL.DURATION: Represents the duration of activities in various formats, enhancing task scheduling capabilities.
GLOBAL.LOCATION: Extracts location information from user inputs, streamlining context-based queries.
GLOBAL.PERSON: Identifies names, enabling personalized interactions within the Virtual Agent.
GLOBAL.MONEY: Captures financial amounts and currency types for financial inquiries and transactions.
GLOBAL.NUMBER: Identifies numerical values, enhancing data-driven interactions.
GLOBAL.SOFTWARE: Extracts software names to assist users with related queries.
GLOBAL.HARDWARE: Identifies hardware references, improving support for technical inquiries.

Key Outcomes

By utilizing these NLU system entities, ServiceNow customers can expect improved interaction accuracy within the Virtual Agent, resulting in:

Enhanced customer experience through precise understanding of user queries.
Streamlined processes by automating date, time, location, and financial information extraction.
Increased operational efficiency as the Virtual Agent can handle a wider range of inquiries accurately.

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
                }
            ]
        }
    ]
}