\(Legacy\) NLU system entities
reference brazil Updated Sep 10, 2026 5 min read nlu-system-entities.md
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.
|
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 |
JSON |
|
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 |
JSON |
|
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 |
JSON |
|
Usage |
Example |
|---|---|
|
Format |
YYYY |
|
Regular expression |
\\d\\d\\d\\d |
|
Input example |
Mr. Smith left in 1999. |
|
Normalized value |
1999 |
|
Code example |
JSON |
|
Usage |
Example |
|---|---|
|
Format |
One of the following:- Winter: YYYYWI
|
|
Regular expression |
One of the following:- Winter: \\d\\d\\d\\dWI
|
|
Input example |
Mr. Smith left in the fall of 1999. |
|
Normalized value |
1999FA |
|
Code example |
JSON |
The TIME SubType returns a time string that is accurate to an hour and a minute.
|
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 |
JSON |
|
Usage |
Example |
|---|---|
|
Format |
One of the following:- Morning: TMO
|
|
Regular expression |
One of the following:- Morning: TMO
|
|
Input example |
Mr. Smith left in the morning. |
|
Normalized value |
TMO |
|
Code example |
JSON |
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.
|
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 |
JSON |
This entity returns a duration string that specifies the duration of the activity.
|
Usage |
Example |
|---|---|
|
Format |
One of the following:- Second: 's'ss
|
|
Regular expression |
One of the following:- Second: s\\d\\d
|
|
Input example |
Mr. Smith stayed in Boston for 48 hours. |
|
Normalized value |
h48 |
|
Code example |
JSON |
This entity returns a location string.
|
Usage |
Example |
|---|---|
|
Format |
String value. Example: |
|
Regular expression |
Not applicable. |
|
Input example |
Mr. Smith works in Santa Clara. |
|
Normalized value |
Santa Clara |
|
Code example |
JSON |
This entity returns a name string.
|
Usage |
Example |
|---|---|
|
Format |
String value. Example: |
|
Regular expression |
Not applicable. |
|
Input example |
Joe Smith works in Santa Clara. |
|
Normalized value |
Joe Smith |
|
Code example |
JSON |
This entity returns a currency string.
|
Usage |
Example |
|---|---|
|
Format |
String value. Example: |
|
Regular expression |
Not applicable. |
|
Input example |
Show me laptops for less than $2000. |
|
Normalized value |
USD 2000Note: The normalized value uses the three-letter ISO 3166 country code of the source currency. |
|
Code example |
Shell |
This entity returns a number.
|
Usage |
Example |
|---|---|
|
Format |
String value. Example: |
|
Regular expression |
Not applicable. |
|
Input example |
I want to see the previous 5 transactions from my account. |
|
Normalized value |
5.0 |
|
Code example |
Shell |
Returns a software string.
|
Usage |
Example |
|---|---|
|
Format |
String value. Example: |
|
Regular expression |
Not applicable. |
|
Input example |
How do I install Java? |
|
Normalized value |
Java |
|
Code example |
JSON |
Returns a hardware string.
|
Usage |
Example |
|---|---|
|
Format |
String value. Example: |
|
Regular expression |
Not applicable. |
|
Input example |
How do I order a printer? |
|
Normalized value |
printer |
|
Code example |
JSON |
JSON
{"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
JSON
{ "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 } ] } ]}Parent Topic:(Legacy) Virtual Agent NLU technical reference
1,031 lines · 14.1 KB
---
title: \(Legacy\) NLU system entities
description: 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.
locale: en-US
canonical_url: https://www.servicenow.com/docs/r/conversational-interfaces/nlu-system-entities.html
release: brazil
topic_type: reference
last_updated: "2026-09-10"
reading_time_minutes: 5
breadcrumb: [NLU technical reference, NLU reference, \(Legacy\) Virtual Agent for NLU, Conversational Interfaces]
---
# \(Legacy\) NLU system entities
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 id="table_lcl_cjd_lpb"><thead><tr><th>
Usage
</th><th>
Example
</th></tr></thead><tbody><tr><td>
Format
</td><td>
YYYY-MM-DD
</td></tr><tr><td>
Regular expression
</td><td>
\\\\d\\\\d\\\\d\\\\d-\\\\d\\\\d-\\\\d\\\\d
</td></tr><tr><td>
Input example
</td><td>
Mr. Smith left Friday, February 4, 2019.
</td></tr><tr><td>
Normalized value
</td><td>
2019-02-04
</td></tr><tr><td>
Code example
</td><td>
```
{
"name": "DATE",
"value": "...",
"score": 1.0,
"normalization": "2019-02-04"
}
```
</td></tr></tbody>
</table>The WEEK SubType returns a date string of a specific week of a year.
<table id="table_crg_tjd_lpb"><thead><tr><th>
Usage
</th><th>
Example
</th></tr></thead><tbody><tr><td>
Format
</td><td>
YYYY'W'WW
</td></tr><tr><td>
Regular expression
</td><td>
\\\\d\\\\d\\\\d\\\\d\\\\dW\\\\d\\\\d
</td></tr><tr><td>
Input example
</td><td>
Mr. Smith left the third week of 1999.
</td></tr><tr><td>
Normalized value
</td><td>
1999W3
</td></tr><tr><td>
Code example
</td><td>
```
{
"name": "entity:GLOBAL.DATE",
"value": "...",
"score": 1.0,
"normalization": {"type": "GLOBAL.DATE", "subType": "WEEK", "value":"1999W3"}
}
```
</td></tr></tbody>
</table>The MONTH SubType returns a date string of a specific month of a year.
<table id="table_edh_sld_lpb"><thead><tr><th>
Usage
</th><th>
Example
</th></tr></thead><tbody><tr><td>
Format
</td><td>
YYYY'M'MM
</td></tr><tr><td>
Regular expression
</td><td>
\\\\d\\\\d\\\\d\\\\dM\\\\d\\\\d
</td></tr><tr><td>
Input example
</td><td>
Mr. Smith left in February of 1999.
</td></tr><tr><td>
Normalized value
</td><td>
1999M02
</td></tr><tr><td>
Code example
</td><td>
```
{
"name": "entity:GLOBAL.DATE",
"value": "...",
"score": 1.0,
"normalization": {"type": "GLOBAL.DATE", "subType": "MONTH", "value": "1999M02"}
}
```
</td></tr></tbody>
</table>The YEAR SubType returns a date string of a specific year.
<table id="table_pxj_1md_lpb"><thead><tr><th>
Usage
</th><th>
Example
</th></tr></thead><tbody><tr><td>
Format
</td><td>
YYYY
</td></tr><tr><td>
Regular expression
</td><td>
\\\\d\\\\d\\\\d\\\\d
</td></tr><tr><td>
Input example
</td><td>
Mr. Smith left in 1999.
</td></tr><tr><td>
Normalized value
</td><td>
1999
</td></tr><tr><td>
Code example
</td><td>
```
{
"name": "entity:GLOBAL.DATE",
"value": "...",
"score": 1.0,
"normalization": {"type": "GLOBAL.DATE", "subType": "YEAR", "value": "1999"}
}
```
</td></tr></tbody>
</table>The SEASON SubType returns a date string of a specific season of the year.
<table id="table_s1q_fpd_lpb"><thead><tr><th>
Usage
</th><th>
Example
</th></tr></thead><tbody><tr><td>
Format
</td><td>
One of the following:- **Winter**: YYYYWI
- **Spring**: YYYYSP
- **Summer**: YYYYSU
- **Fall**: YYYYFA
</td></tr><tr><td>
Regular expression
</td><td>
One of the following:- **Winter**: \\\\d\\\\d\\\\d\\\\dWI
- **Spring**: \\\\d\\\\d\\\\d\\\\dSP
- **Summer**: \\\\d\\\\d\\\\d\\\\dSU
- **Fall**: \\\\d\\\\d\\\\d\\\\dFA
</td></tr><tr><td>
Input example
</td><td>
Mr. Smith left in the fall of 1999.
</td></tr><tr><td>
Normalized value
</td><td>
1999FA
</td></tr><tr><td>
Code example
</td><td>
```
{
"name": "entity:GLOBAL.DATE",
"value": "...",
"score": 1.0,
"normalization": {"type": "GLOBAL.DATE", "subType": "SEASON", "value": "1999FA"}
}
```
</td></tr></tbody>
</table>## GLOBAL.TIME system entity
The TIME SubType returns a time string that is accurate to an hour and a minute.
<table id="table_zrm_mqd_lpb"><thead><tr><th>
Usage
</th><th>
Example
</th></tr></thead><tbody><tr><td>
Format
</td><td>
'T'HH:mm
</td></tr><tr><td>
Regular expression
</td><td>
T\\\\d\\\\d:\\\\d\\\\d
</td></tr><tr><td>
Input example
</td><td>
Mr. Smith left at ten minutes to three.
</td></tr><tr><td>
Normalized value
</td><td>
T02:50
</td></tr><tr><td>
Code example
</td><td>
```
{
"name": "entity:GLOBAL.TIME",
"value": "...",
"score": 1.0,
"normalization": {"type": "GLOBAL.TIME","subType": "TIME", "value": "T02:50"}
}
```
</td></tr></tbody>
</table>The PARTSOFDAY SubType returns a time string that specifies parts of the day.
<table id="table_thr_wqd_lpb"><thead><tr><th>
Usage
</th><th>
Example
</th></tr></thead><tbody><tr><td>
Format
</td><td>
One of the following:- **Morning**: TMO
- **Afternoon**: TAF
- **Evening**: TEV
- **Night**: TNI
</td></tr><tr><td>
Regular expression
</td><td>
One of the following:- **Morning**: TMO
- **Afternoon**: TAF
- **Evening**: TEV
- **Night**: TNI
</td></tr><tr><td>
Input example
</td><td>
Mr. Smith left in the morning.
</td></tr><tr><td>
Normalized value
</td><td>
TMO
</td></tr><tr><td>
Code example
</td><td>
```
{
"name": "entity:GLOBAL.TIME",
"value": "...",
"score": 1.0,
"normalization": {"type": "GLOBAL.TIME", "subType": "PARTSOFDAY", "value": "TMO"}
}
```
</td></tr></tbody>
</table>## 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 id="table_wsh_ybc_3vb"><thead><tr><th>
Usage
</th><th>
Example
</th></tr></thead><tbody><tr><td>
Format
</td><td>
YYYY-MM-DD'T'HH:mm
</td></tr><tr><td>
Regular expression
</td><td>
\\\\d\\\\d\\\\d\\\\d-\\\\d\\\\d-\\\\d\\\\dT\\\\d\\\\d:\\\\d\\\\d
</td></tr><tr><td>
Input example
</td><td>
Mr. Smith leaves on October 31st at 5:00 p.m.
</td></tr><tr><td>
Normalized value
</td><td>
2022-10-31T17:00
</td></tr><tr><td>
Code example
</td><td>
```
{
"name": "DATE_TIME",
"value": "October 31st at 5:00 p.m",
"normalization": "2022-10-31T17:00","confidence": "1"
}
```
</td></tr></tbody>
</table>## GLOBAL.DURATION system entity
This entity returns a duration string that specifies the duration of the activity.
<table id="table_gf3_mrd_lpb"><thead><tr><th>
Usage
</th><th>
Example
</th></tr></thead><tbody><tr><td>
Format
</td><td>
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
</td></tr><tr><td>
Regular expression
</td><td>
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
</td></tr><tr><td>
Input example
</td><td>
Mr. Smith stayed in Boston for 48 hours.
</td></tr><tr><td>
Normalized value
</td><td>
h48
</td></tr><tr><td>
Code example
</td><td>
```
{
"name": "entity:GLOBAL.DURATION",
"value": "...",
"score": 1.0,
"normalization": {"type": "GLOBAL.DURATION", "value": "h48"}
}
```
</td></tr></tbody>
</table>## GLOBAL.LOCATION system entity
This entity returns a location string.
<table id="table_wd5_cvd_lpb"><thead><tr><th>
Usage
</th><th>
Example
</th></tr></thead><tbody><tr><td>
Format
</td><td>
String value. Example: `Santa Clara`
</td></tr><tr><td>
Regular expression
</td><td>
Not applicable.
</td></tr><tr><td>
Input example
</td><td>
Mr. Smith works in Santa Clara.
</td></tr><tr><td>
Normalized value
</td><td>
Santa Clara
</td></tr><tr><td>
Code example
</td><td>
```
{
"name": "entity:GLOBAL.LOCATION",
"value": "...",
"score": 1.0,
"normalization": {"type": "GLOBAL.LOCATION", "value":"Santa Clara"}
}
```
</td></tr></tbody>
</table>## GLOBAL.PERSON system entity
This entity returns a name string.
<table id="table_og1_nvd_lpb"><thead><tr><th>
Usage
</th><th>
Example
</th></tr></thead><tbody><tr><td>
Format
</td><td>
String value. Example: `Joe Smith`
</td></tr><tr><td>
Regular expression
</td><td>
Not applicable.
</td></tr><tr><td>
Input example
</td><td>
Joe Smith works in Santa Clara.
</td></tr><tr><td>
Normalized value
</td><td>
Joe Smith
</td></tr><tr><td>
Code example
</td><td>
```
{
"name": "entity:GLOBAL.PERSON",
"value": "...",
"score": 1.0,
"normalization": {"type": "GLOBAL.PERSON", "value":"Joe Smith"}
}
```
</td></tr></tbody>
</table>## GLOBAL.MONEY system entity
This entity returns a currency string.
<table id="table_c1b_wvd_lpb"><thead><tr><th>
Usage
</th><th>
Example
</th></tr></thead><tbody><tr><td>
Format
</td><td>
String value. Example: `USD 2000`
</td></tr><tr><td>
Regular expression
</td><td>
Not applicable.
</td></tr><tr><td>
Input example
</td><td>
Show me laptops for less than $2000.
</td></tr><tr><td>
Normalized value
</td><td>
USD 2000**Note:** The normalized value uses the three-letter ISO 3166 country code of the source currency.
</td></tr><tr><td>
Code example
</td><td>
```
{
"name": "entity:GLOBAL.MONEY",
"value": "...",
"score": 1.0,
"normalization": {"type": "GLOBAL.MONEY", "value":"2000", “currency”:”USD”}
}
```
</td></tr></tbody>
</table>## GLOBAL.NUMBER system entity
This entity returns a number.
<table id="table_jxp_byd_lpb"><thead><tr><th>
Usage
</th><th>
Example
</th></tr></thead><tbody><tr><td>
Format
</td><td>
String value. Example: `5.0`
</td></tr><tr><td>
Regular expression
</td><td>
Not applicable.
</td></tr><tr><td>
Input example
</td><td>
I want to see the previous 5 transactions from my account.
</td></tr><tr><td>
Normalized value
</td><td>
5.0
</td></tr><tr><td>
Code example
</td><td>
```
{
"name": "entity:GLOBAL.NUMBER",
"value": "...",
"score": 1.0,
"normalization": {"numericValue":"5", “normalizedValue”: “5”}
}
```
</td></tr></tbody>
</table>## GLOBAL.SOFTWARE
Returns a software string.
<table id="table_oq4_vcy_brb"><thead><tr><th>
Usage
</th><th>
Example
</th></tr></thead><tbody><tr><td>
Format
</td><td>
String value. Example: `Java`
</td></tr><tr><td>
Regular expression
</td><td>
Not applicable.
</td></tr><tr><td>
Input example
</td><td>
How do I install Java?
</td></tr><tr><td>
Normalized value
</td><td>
Java
</td></tr><tr><td>
Code example
</td><td>
```
{
"name": "entity:GLOBAL.SOFTWARE",
"value": "Java",
"score": 0.99930537,
"normalization": {"type":"entity:GLOBAL.SOFTWARE",
"subType":"SOFTWARE",
"value":"Java"}
}
```
</td></tr></tbody>
</table>## GLOBAL.HARDWARE
Returns a hardware string.
<table id="table_dfl_ycy_brb"><thead><tr><th>
Usage
</th><th>
Example
</th></tr></thead><tbody><tr><td>
Format
</td><td>
String value. Example: `printer`
</td></tr><tr><td>
Regular expression
</td><td>
Not applicable.
</td></tr><tr><td>
Input example
</td><td>
How do I order a printer?
</td></tr><tr><td>
Normalized value
</td><td>
printer
</td></tr><tr><td>
Code example
</td><td>
```
{
"name": "entity:GLOBAL.HARDWARE",
"value": "printer",
"score": 1.0,
"normalization": {"type":"entity:GLOBAL.HARDWARE",
"subType":"HARDWARE",
"value":"printer"}
}
```
</td></tr></tbody>
</table>## 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
}
]
}
]
}
```
**Parent Topic:**[\(Legacy\) Virtual Agent NLU technical reference](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/brazil/markdown/conversational-interfaces/va-advanced-technical-reference-nlu.md)
| title | \(Legacy\) NLU system entities |
| description | 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. |
| locale | en-US |
| canonical_url | https://www.servicenow.com/docs/r/conversational-interfaces/nlu-system-entities.html |
| release | brazil |
| topic_type | reference |
| last_updated | 2026-09-10 |
| reading_time_minutes | 5 |
| breadcrumb | NLU technical referenceNLU reference\(Legacy\) Virtual Agent for NLUConversational Interfaces |