---
sourceDocument: Yokohama Conversational Interfaces
sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/yokohama/conversational-interfaces

 Release :

    - yokohama

ft:locale :

    - en-US

ft:publication_title :

    - Yokohama Conversational Interfaces

ft:clusterId :

    - convint

bundleId :

    - convint

workflow :

    - Platform


---

# NLU system entities

# NLU system entities {#ariaid-title1}

Release version: Yokohama  
Updated January 30, 2025  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 5 minutes to read
Summarize  
![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI  
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'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.
Show full answer Show less  

## 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 {#nlu-system-entities__section_cqn_53d_lpb}

The DAY SubType returns a date string that is accurate to a specific date.  
{#nlu-system-entities__table_lcl_cjd_lpb__entry__2}

| 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" } |
[Table 1. GLOBAL.DATE SubType = DAY usage]

{#nlu-system-entities__table_lcl_cjd_lpb}

The WEEK SubType returns a date string of a specific week of a year.  
{#nlu-system-entities__table_crg_tjd_lpb__entry__2}

| 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"} } |
[Table 2. GLOBAL.DATE SubType = WEEK usage]

{#nlu-system-entities__table_crg_tjd_lpb}

The MONTH SubType returns a date string of a specific month of a year.  
{#nlu-system-entities__table_edh_sld_lpb__entry__2}

| 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"} } |
[Table 3. GLOBAL.DATE SubType = MONTH usage]

{#nlu-system-entities__table_edh_sld_lpb}

The YEAR SubType returns a date string of a specific year.  
{#nlu-system-entities__table_pxj_1md_lpb__entry__2}

| 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"} } |
[Table 4. GLOBAL.DATE SubType = YEAR usage]

{#nlu-system-entities__table_pxj_1md_lpb}

The SEASON SubType returns a date string of a specific season of the year.  
{#nlu-system-entities__table_s1q_fpd_lpb__entry__2}

| Usage | Example |
|-|-|
| Format | One of the following: * Winter: YYYYWI * Spring: YYYYSP * Summer: YYYYSU * Fall: YYYYFA {#nlu-system-entities__ul_m4j_jpd_lpb} |
| 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 {#nlu-system-entities__ul_cnh_npd_lpb} |
| 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"} } |
[Table 5. GLOBAL.DATE SubType = SEASON usage]

{#nlu-system-entities__table_s1q_fpd_lpb}

## GLOBAL.TIME system entity {#nlu-system-entities__section_rrk_kqd_lpb}

The TIME SubType returns a time string that is accurate to an hour and a minute.  
{#nlu-system-entities__table_zrm_mqd_lpb__entry__2}

| 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"} } |
[Table 6. GLOBAL.TIME SubType = TIME usage]

{#nlu-system-entities__table_zrm_mqd_lpb}

The PARTSOFDAY SubType returns a time string that specifies parts of the day.  
{#nlu-system-entities__table_thr_wqd_lpb__entry__2}

| Usage | Example |
|-|-|
| Format | One of the following: * Morning: TMO * Afternoon: TAF * Evening: TEV * Night: TNI {#nlu-system-entities__ul_xl5_crd_lpb} |
| Regular expression | One of the following: * Morning: TMO * Afternoon: TAF * Evening: TEV * Night: TNI {#nlu-system-entities__ul_omc_grd_lpb} |
| 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"} } |
[Table 7. GLOBAL.TIME SubType = PARTSOFDAY usage]

{#nlu-system-entities__table_thr_wqd_lpb}

## GLOBAL.DATE_TIME system entity {#nlu-system-entities__section_rkt_pbc_3vb}

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.  
{#nlu-system-entities__table_wsh_ybc_3vb__entry__2}

| 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" } |
[Table 8. GLOBAL.DATE_TIME SubType = DATETIME usage]

{#nlu-system-entities__table_wsh_ybc_3vb}

## GLOBAL.DURATION system entity {#nlu-system-entities__section_qhq_jrd_lpb}

This entity returns a duration string that specifies the duration of the activity.  
{#nlu-system-entities__table_gf3_mrd_lpb__entry__2}

| 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 {#nlu-system-entities__ul_dkp_qrd_lpb} |
| 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 {#nlu-system-entities__ul_bcb_yrd_lpb} |
| 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"} } |
[Table 9. GLOBAL.DURATION usage]

{#nlu-system-entities__table_gf3_mrd_lpb}

## GLOBAL.LOCATION system entity {#nlu-system-entities__section_wpj_2sd_lpb}

This entity returns a location string.  
{#nlu-system-entities__table_wd5_cvd_lpb__entry__2}

| 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"} } |
[Table 10. GLOBAL.LOCATION usage]

{#nlu-system-entities__table_wd5_cvd_lpb}

## GLOBAL.PERSON system entity {#nlu-system-entities__section_b1x_jvd_lpb}

This entity returns a name string.  
{#nlu-system-entities__table_og1_nvd_lpb__entry__2}

| 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"} } |
[ ]

{#nlu-system-entities__table_og1_nvd_lpb}

## GLOBAL.MONEY system entity {#nlu-system-entities__section_xkc_5vd_lpb}

This entity returns a currency string.  
{#nlu-system-entities__table_c1b_wvd_lpb__entry__2}

| 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"} } |
[Table 11. GLOBAL.MONEY usage]

{#nlu-system-entities__table_c1b_wvd_lpb}

## GLOBAL.NUMBER system entity {#nlu-system-entities__section_fn2_ywd_lpb}

This entity returns a number.  
{#nlu-system-entities__table_jxp_byd_lpb__entry__2}

| 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"} } |
[ ]

{#nlu-system-entities__table_jxp_byd_lpb}

## GLOBAL.SOFTWARE {#nlu-system-entities__section_cr2_scy_brb}

Returns a software string.  
{#nlu-system-entities__table_oq4_vcy_brb__entry__2}

| 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"} } |
[ ]

{#nlu-system-entities__table_oq4_vcy_brb}

## GLOBAL.HARDWARE {#nlu-system-entities__section_bdt_xcy_brb}

Returns a hardware string.  
{#nlu-system-entities__table_dfl_ycy_brb__entry__2}

| 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"} } |
[ ]

{#nlu-system-entities__table_dfl_ycy_brb}

## Example NLU prediction result using Software system
entity {#nlu-system-entities__section_hlp_xdy_brb}

    {"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 {#nlu-system-entities__section_f4c_gxd_lpb}

    {
        "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
                    }
                ]
            }
        ]
    }

**Related concepts**   

* [Domain separation and Virtual Agent](https://servicenow-prod.fluidtopics.net/E1j8F9a6Fe4dLE0drB0a_w "Domain separation is supported in the Virtual Agent application. Domain separation enables you to separate data, processes, and administrative tasks into logical groupings called domains. You can control several aspects of this separation, including which users can see and access data.")
* [Virtual Agent interaction records](https://servicenow-prod.fluidtopics.net/ZuuRpg0W0qr6qLZSr9CEqA "Each time a Virtual Agent conversation occurs, an interaction record captures the entire conversation in the Interactions [interaction] table. The record includes all topic elements used in the conversation, as well as live agent transfers.")
* [Virtual Agent scripts](https://servicenow-prod.fluidtopics.net/ke~GUHWgbfbG1gVpGBXv6A "Use ServiceNow Virtual Agent script methods and variables to write chat scripts, such as response, trigger, and flow scripts. Variables can also provide context for your live support topics and conversations.")  
**Related reference**   

* [Virtual Agent roles](https://servicenow-prod.fluidtopics.net/ADLLISrHgiW5P15~sMGPeQ#VA-roles "Virtual Agent adds several roles for users operating as Virtual Agent administrators.")
* [Input data types in Virtual Agent topics](https://servicenow-prod.fluidtopics.net/ez3sicwydv8MfCVtr3s8~g "You can define inputs of commonly used Glide Virtual Agent (com.glide.cs.chatbot) data types in Virtual Agent Designer without writing a script. Define the input data type on the Start node of a custom control or topic block.")
* [Virtual Agent URL parameters](https://servicenow-prod.fluidtopics.net/vNKDkTdIOc8plnioeUmuqQ "Virtual Agent provides various system parameters that admins can add to an instance URL to control how page content is rendered.")
* [Latency feedback in Virtual Agent](https://servicenow-prod.fluidtopics.net/f_41JtGwhllDIecVv95iBg "The com.glide.cs.message.processing.enabled system property notifies requesters whenever the generative AI large language model (LLM) is processing their request in the Virtual Agent chat widget and Now Assist panel.")