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API calls

[TOC]

List of entity types

Following are the list of different entity types along with its API call:

text

  • This functionality calls the TextDetection class to detect textual entities.

  • Example:

    • Example 1:

      • message='i want to order chinese from  mainland china and pizza from domminos'
entity_name='restaurant'
structured_value=None
fallback_value=None
bot_message=None

      • Python:

        from ner_v1.chatbot.entity_detection import get_text
output = get_text(message=message, entity_name=entity_name, structured_value=structured_value, fallback_value=fallback_value, bot_message=bot_message)
print output

        The above can also be done from within the Docker container's shell. Setup is in docker.md file.

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/text/?message=i%20want%20to%20order%20chinese%20from%20%20mainland%20china%20and%20pizza%20from%20domminos&entity_name=restaurant&structured_value=&fallback_value=None&bot_message='

      • CURL Output:

        {
  "data": [
    {
      "detection": "message",
      "original_text": "mainland china",
      "entity_value": {
        "value": "Mainland China"
      }
    },
    {
      "detection": "message",
      "original_text": "dominos",
      "entity_value": {
        "value": "Domino's Pizza"
      }
    }
  ]
}

    • Example 2:

      • message = 'i wanted to watch movie'
entity_name = 'movie'
structured_value = 'inferno'
fallback_value = None
bot_message = None

      • Python:

        from ner_v1.chatbot.entity_detection import get_text
output = get_text(message=message, entity_name=entity_name, structured_value=structured_value, fallback_value=fallback_value, bot_message=bot_message)
print output

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/text/?message=i%20wanted%20to%20watch%20movie&entity_name=movie&structured_value=inferno&fallback_value=None&bot_message='

      • CURL Output:

      • ​

        {
  "data": [
    {
      "detection": "structure_value_verified",
      "original_text": "inferno",
      "entity_value": {
        "value": "Inferno"
      }
    }
  ]
}

    • ​

phone_number

  • This functionality calls the PhoneDetector class to detect textual entities.

  • Example:

    • Example 1:

      • message = 'my contact number is 9049961794'
entity_name = 'phone_number'
structured_value = None
fallback_value = None
bot_message = None

      • Python:

        from ner_v1.chatbot.entity_detection import get_phone_number
output = get_phone_number(message=message, entity_name=entity_name, structured_value=structured_value, fallback_value=fallback_value, bot_message=bot_message)
print output

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/phone_number/?message=my%20contact%20number%20is%209049961794&entity_name=phone_number&structured_value=&fallback_value=None&bot_message='

      • CURL Output:

        {
  "data": [
    {
      "detection": "message",
      "original_text": "9049961794",
      "entity_value": {
        "value": "9049961794"
      }
    }
  ]
}

    • Example 2:

      • message = 'Please call me'
entity_name = 'phone_number'
structured_value = None
fallback_value = '9049961794'
bot_message = None

      • Python:

        from ner_v1.chatbot.entity_detection import get_phone_number
output = get_phone_number(message=message, entity_name=entity_name, structured_value=structured_value, fallback_value=fallback_value, bot_message=bot_message)
print output

      • CURL command:

        URL='localhost'
PORT=8081

        ​

        curl -i 'http://'$URL':'$PORT'/v1/phone_number/?message=Please%20call%20me&entity_name=phone_number&structured_value=&fallback_value=9049961794&bot_message='

      • CURL Output:

        {
  "data": [
    {
      "detection": "fallback_value",
      "original_text": "9049961794",
      "entity_value": {
        "value": "9049961794"
      }
    }
  ]
}

email

  • This functionality calls the EmailDetector class to detect email ids.

  • Example:

    • Example 1:

      • message = 'my email id is [email protected]'
entity_name = 'email'
structured_value = None
fallback_value = None
bot_message = None

      • Python:

        from ner_v1.chatbot.entity_detection import get_email
output = get_email(message=message, entity_name=entity_name, structured_value=structured_value,fallback_value=fallback_value, bot_message=bot_message)
print output

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/email/?message=my%20email%20id%20is%20apurv.nagvenkar%40gmail.com&entity_name=email&structured_value=&fallback_value=&bot_message='

      • CURL Output:

        {
  "data": [
    {
      "detection": "message",
      "original_text": "[email protected]",
      "entity_value": {
        "value": "[email protected]"
      }
    }
  ]
}

    • Example 2:

      • message = 'send me to my email'
entity_name = 'email'
structured_value = None
fallback_value = '[email protected]'
bot_message = None

      • Python:

        from ner_v1.chatbot.entity_detection import get_email
output = get_email(message=message, entity_name=entity_name, structured_value=structured_value,fallback_value=fallback_value, bot_message=bot_message)
print output

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/email/?message=send%20me%20to%20my%20email&entity_name=email&structured_value=&[email protected]&bot_message='

      • CURL Output:

        {
  "data": [
    {
      "detection": "fallback_value",
      "original_text": "[email protected]",
      "entity_value": {
        "value": "[email protected]"
      }
    }
  ]
}

city

  • This functionality calls the CityDetector class to detect cities along with its attributes.

  • Example:

    • Example 1:

      • message = 'i want to go to mummbai'
entity_name = 'city'
structured_value = None
fallback_value = None
bot_message = None

      • Python:

        from ner_v1.chatbot.entity_detection import get_city
output = get_city(message=message, entity_name=entity_name, structured_value=structured_value,fallback_value=fallback_value,bot_message=bot_message)
print output

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/city/?message=i%20want%20to%20go%20to%20mummbai&entity_name=city&structured_value=&fallback_value=&bot_message='

      • CURL Output:

        {
  "data": [
    {
      "detection": "message",
      "original_text": "mummbai",
      "entity_value": {
        "to": true,
        "via": false,
        "from": false,
        "value": "Mumbai",
        "normal": false
      }
    }
  ]
}

    • Example 2:

      • message = "I want to book a flight from delhhi to mumbai"
entity_name = 'city'
structured_value = None
fallback_value = None
bot_message = None

      • Python:

        from ner_v1.chatbot.entity_detection import get_city
output = get_city(message=message, entity_name=entity_name, structured_value=structured_value,fallback_value=fallback_value,bot_message=bot_message)
print output

      • CURL command:*

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/city/?message=I%20want%20to%20book%20a%20flight%20from%20delhhi%20to%20mumbai&entity_name=city&structured_value=&fallback_value=&bot_message='

      • CURL Output:

        {
  "data": [
    {
      "detection": "message",
      "original_text": "delhhi",
      "entity_value": {
        "to": false,
        "via": false,
        "from": true,
        "value": "New Delhi",
        "normal": false
      }
    },
    {
      "detection": "message",
      "original_text": "mumbai",
      "entity_value": {
        "to": true,
        "via": false,
        "from": false,
        "value": "Mumbai",
        "normal": false
      }
    }
  ]
}

    • Example 3:

      • message = "mummbai"
entity_name = 'city'
structured_value = None
fallback_value = None
bot_message = "Please help me departure city?"

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/city/?message=mummbai&entity_name=city&structured_value=&fallback_value=&bot_message=Please%20help%20me%20departure%20city%3F'

      • CURL Output:

        {
  "data": [
    {
      "detection": "message",
      "original_text": "mummbai",
      "entity_value": {
        "to": false,
        "via": false,
        "from": true,
        "value": "Mumbai",
        "normal": false
      }
    }
  ]
}

pnr

  • This functionality calls the PNRDetector class to detect pnr.

  • Example:

    • Example 1:

      • message = 'check my pnr status for 2141215305.'
entity_name = 'train_pnr'
structured_value = None
fallback_value = None
bot_message = None

      • Python:

        from ner_v1.chatbot.entity_detection import get_pnr
output = get_pnr(message=message, entity_name=entity_name,structured_value=structured_value,fallback_value=fallback_value, bot_message=bot_message)
print output

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/pnr/?message=check%20my%20pnr%20status%20for%202141215305.&entity_name=pnr&structured_value=&fallback_value=&bot_message='

      • CURL Output:

        {
  "data": [
    {
      "detection": "message",
      "original_text": "2141215305",
      "entity_value": {
        "value": "2141215305"
      }
    }
  ]
}

number

  • This functionality calls the NumberDetector class to detect numerals.

  • Example:

    • Example 1:

      • message = "I want to purchase 30 units of mobile and 40 units of Television"
entity_name = 'number_of_unit'
structured_value = None
fallback_value = None
bot_message = None
min_digit = 1
max_digit = 2

      • Python:

        from ner_v1.chatbot.entity_detection import get_number
output = get_number(message=message, entity_name=entity_name, structured_value=structured_value, fallback_value=fallback_value, bot_message=bot_message, min_digit=min_digit, max_digit=max_digit)
print output

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/number/?message=I%20want%20to%20purchase%2030%20units%20of%20mobile%20and%2040%20units%20of%20Television&entity_name=number_of_unit&structured_value=&fallback_value=&bot_message=&min_number_digits=1&max_number_digits=2'

      • CURL Output:

        {
  "data": [
    {
      "detection": "message",
      "original_text": "30",
      "entity_value": {
        "value": "30"
      }
    },
    {
      "detection": "message",
      "original_text": "40",
      "entity_value": {
        "value": "40"
      }
    }
  ]
}

    • Example 2:

      • message = "I want to reserve a table for 3 people"
entity_name = 'number_of_people'
structured_value = None
fallback_value = None
bot_message = None
min_digit = 1
max_digit = 2

      • Python:

        from ner_v1.chatbot.entity_detection import get_number
output = get_number(message=message, entity_name=entity_name, structured_value=structured_value, fallback_value=fallback_value, bot_message=bot_message, min_digit=min_digit, max_digit=max_digit)
print output

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/number/?message=I%20want%20to%20reserve%20a%20table%20for%203%20people&entity_name=number_of_people&structured_value=&fallback_value=&bot_message=&min_number_digits=1&max_number_digits=2'

      • CURL Output:

        {
  "data": [
    {
      "detection": "message",
      "original_text": "for 3 people",
      "entity_value": {
        "value": "3"
      }
    }
  ]
}

time

  • This functionality calls the TimeDetector class to detect time.

  • Example:

    • Example 1:

      • Use the timezone parameter to pass your current timezone to time detection

      • message = "John arrived at the bus stop at 13:50 hrs, expecting the bus to be there in 15 mins. \
But the bus was scheduled for 12:30 pm"
entity_name = 'time'
structured_value = None
fallback_value = None
bot_message = None
timezone = 'UTC'

      • Python:

        from ner_v1.chatbot.entity_detection import get_time
output = get_time(message=message, entity_name=entity_name, structured_value=structured_value, fallback_value=fallback_value, bot_message=bot_message, timezone=timezone)
print output

      • CURL command:*

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/time/?message=John%20arrived%20at%20the%20bus%20stop%20at%2013%3A50%20hrs%2C%20expecting%20the%20bus%20to%20be%20there%20in%2015%20mins.%20But%20the%20bus%20was%20scheduled%20for%2012%3A30%20pm&entity_name=time&structured_value=&fallback_value=&bot_message=&timezone=UTC'

      • CURL Output:

        {
  "data": [
    {
      "detection": "message",
      "original_text": "12:30 pm",
      "entity_value": {
        "mm": 30,
        "hh": 12,
        "nn": "pm"
      }
    },
    {
      "detection": "message",
      "original_text": "in 15 mins",
      "entity_value": {
        "mm": "15",
        "hh": 0,
        "nn": "df"
      }
    },
    {
      "detection": "message",
      "original_text": "13:50",
      "entity_value": {
        "mm": 50,
        "hh": 13,
        "nn": "hrs"
      }
    }
  ]
}

time_with_range

  • This functionality calls the TimeDetector class to detect time with range.

  • Example:

    • Example 1:

      • Use the timezone parameter to pass your current timezone to time detection

      • message = 'Set a drink water reminder for tomorrow from 7:00 AM to 6:00 PM'
entity_name = 'time_with_range'
structured_value = None
fallback_value = None
bot_message = None
timezone = 'UTC'

      • Python:

        from ner_v1.chatbot.entity_detection import get_time_with_range
output = get_time_with_range(message=message, entity_name=entity_name, structured_value=structured_value, fallback_value=fallback_value, bot_message=bot_message, timezone=timezone)
print output

      • CURL command:*

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/time_with_range/?message=Set+a+drink+water+reminder+for+tomorrow+from+7%3A00+AM+to+6%3A00+PM&entity_name=time_with_range&structured_value=&fallback_value=&bot_message=&timezone=UTC'

      • CURL Output:

        {
  "data": [
    {
      "detection": "message", 
      "original_text": "7:00 am to 6:00 pm", 
      "entity_value": {
         "mm": 0, 
         "hh": 7, 
         "range": "start", 
         "nn": "am", 
         "time_type": null
      }, 
      "language": "en"
    }, 
    {
      "detection": "message",
      "original_text": "7:00 am to 6:00 pm", 
      "entity_value": {
        "mm": 0, 
        "hh": 6, 
        "range": "end", 
        "nn": "pm", 
        "time_type": null
      }, 
      "language": "en"
    }
    ]
}

date

  • This functionality calls the DateDetector class to detect date.

  • Example:

    • Example 1:

      • Use the timezone parameter to pass your current timezone to date detection

      • message = "set me reminder on 23rd december"
entity_name = 'date'
structured_value = None
fallback_value = None
bot_message = None
timezone='UTC'

      • Python:

        from ner_v1.chatbot.entity_detection import get_date
output = get_date(message=message, entity_name=entity_name, structured_value=structured_value, fallback_value=fallback_value,bot_message=bot_message, timezone=timezone)
print output

      • CURL command:*

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/date/?message=set%20me%20reminder%20on%2023rd%20december&entity_name=date&structured_value=&fallback_value=&bot_message=%timezone=UTC'

      • CURL Output:

        {
    "data": [
      {
        "detection": "message",
        "original_text": "23rd december",
        "entity_value": {
          "end_range": false,
          "from": false,
          "normal": true,
          "value": {
            "mm": 12,
            "yy": 2017,
            "dd": 23,
            "type": "date"
          },
          "to": false,
          "start_range": false
        }
      }
    ]
 }

    • Example 2:

      • message = "set me reminder day after tomorrow"
entity_name = 'date'
structured_value = None
fallback_value = None
bot_message = None
timezone='UTC'

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/date/?message=set%20me%20reminder%20day%20after%20tomorrow&entity_name=date&structured_value=&fallback_value=&bot_message=&timezone=UTC'

      • CURL Output:

        {
    "data": [
      {
        "detection": "message",
        "original_text": "day after tomorrow",
        "entity_value": {
          "end_range": false,
          "from": false,
          "normal": true,
          "value": {
            "mm": 8,
            "yy": 2017,
            "dd": 24,
            "type": "day_after"
          },
          "to": false,
          "start_range": false
        }
      }
    ]
  }

budget

  • This functionality calls the BudgetDetector class to detect budget.

  • Example:

    • Example 1:

      • message = "shirts between 2000 to 3000"
entity_name = 'budget'
structured_value = None
fallback_value = None
bot_message = None

      • Python:

        from ner_v1.chatbot.entity_detection import get_budget
output = get_budget(message=message, entity_name=entity_name, structured_value=structured_value,fallback_value=fallback_value, bot_message=bot_message)
print output

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/budget/?message=shirts%20between%202000%20to%203000&entity_name=budget&structured_value=&fallback_value=&bot_message='

      • CURL Output:

        {
  "data": [
    {
      "detection": "message",
      "original_text": "2000 to 3000",
      "entity_value": {
        "max_budget": 3000,
        "type": "normal_budget",
        "min_budget": 2000
      }
    }
  ]
}

shopping_size

  • This functionality calls the ShoppingSizeDetector class to detect cloth size. For example, Large, small, 34, etc.

  • Example:

    • Example 1:

      • message = "I want to buy Large shirt and jeans of 36 waist"
entity_name = 'shopping_clothes_size'
structured_value = None
fallback_value = None
bot_message = None

      • Python:

        from ner_v1.chatbot.entity_detection import get_shopping_size
output = get_shopping_size(message=message, entity_name=entity_name, structured_value=structured_value, fallback_value=fallback_value, bot_message=bot_message)
print output

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/shopping_size/?message=I%20want%20to%20buy%20Large%20shirt%20and%20jeans%20of%2036%20waist&entity_name=shopping_clothes_size&structured_value=&fallback_value=&bot_message='

      • CURL Output:

        {
  "data": [
    {
      "detection": "message",
      "original_text": "large",
      "entity_value": {
        "value": "L"
      }
    },
    {
      "detection": "message",
      "original_text": "36",
      "entity_value": {
        "value": "36"
      }
    }
  ]
}

passenger_count

  • This functionality calls the PassengerDetector class to detect passenger count.

  • Example:

    • Example 1:

      • message = 'Can you please help me to book tickets for 3 people'
entity_name = 'no_of_adults'
structured_value = None
fallback_value = None
bot_message = None

      • Python:

        from ner_v1.chatbot.entity_detection import get_passenger_count
output = get_passenger_count(message=message, entity_name=entity_name, structured_value=structured_value, fallback_value=fallback_value, bot_message=bot_message)
print output

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/passenger_count/?message=Can+you+please+help+me+to+book+tickets+for+3+people&entity_name=no_of_adults&structured_value=&fallback_value=&bot_message='

      • CURL Output:

        {
  "data": [
    {
      "detection": "message", 
      "original_text": "3", 
      "entity_value": {
        "value": "3"
      }, 
      "language": "en"
    }
  ]
}

location

  • This functionality calls the TextDetector class to detect location.

  • Example:

    • Example 1:

      • message = 'atm in andheri west'
entity_name = 'locality_list'
structured_value = None
fallback_value = None
bot_message = None

      • Python:

        from ner_v1.chatbot.entity_detection import get_location
output = get_location(message=message, entity_name=entity_name, structured_value=structured_value, fallback_value=fallback_value, bot_message=bot_message)
print output

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/location/?message=atm+in+andheri+west&entity_name=locality_list&structured_value=&fallback_value=&bot_message='

      • CURL Output:

        {
  "data": [
    {
      "detection": "message", 
      "original_text": "andheri west", 
      "entity_value": {
        "value": "Andheri West"
      }, 
      "language": "en"
    }
  ]
}

person_name

  • This functionality calls the NameDetector class to detect Name entities.

  • Examples:

    • Example 1:

      • message = 'my name is yash doshi'
entity_name = 'person_name'
structured_value = None
fallback_value = 'Guest'
bot_message = 'what is your name ?'

      • Python:

        from ner_v1.chatbot.entity_detection import get_person_name
output = get_person_name(message=message, entity_name=entity_name, structured_value=structured_value, fallback_value=fallback_value, bot_message=bot_message)
print output

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/person_name/?message=my%20name%20is%yash%20doshi&entity_name=person_name&structured_value=&fallback_value=Guest&bot_message=what%20is%your%20name%20?'

      • CURL Output:

        {
  "data": [
    {
      "detection": "message",
      "original_text": "yash doshi",
      "entity_value": {
        "first_name": "yash",
        "last_name": "doshi",
        "middle_name": null
        
      }
    }
  ]
}

    • Example 2:

      • message = ''
entity_name = 'person_name'
structured_value = None
fallback_value = 'sagar nimesh dedhia'
bot_message = 'what is your name ?'

      • Python:

        from ner_v1.chatbot.entity_detection import get_person_name
output = get_person_name(message=message, entity_name=entity_name, structured_value=structured_value, fallback_value=fallback_value, bot_message=bot_message)
print output

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/person_name/?message=&entity_name=person_name&structured_value=&fallback_value=sagar%20nimesh%20dedhia&bot_message=what%20is%your%20name%20?'

      • CURL Output:

        {
  "data": [
    {
      "detection": "fallback_value",
      "original_text": "sagar nimesh dedhia",
      "entity_value": {
        "first_name": "sagar"
        "last_name": "dedhia"
        "middle_name": "nimesh"
      }
    }
  ]
}

regex

  • This functionality calls the RegexDetector class to detect entities that abide by the specified regex.

    IMPORTANT NOTE:

    1. The regex pattern provided must be escaped if you are not passing in a raw string (marked by 'r' in Python)
    2. Errors in compiling the provided pattern are not handled and will result in an exception
    3. chatbot_ner also uses re.UNICODE flag by default for detection. This can be overridden by using re_flags argument in the constructor
    4. If you are using groups, only 0th group will be returned. Sub grouping is not supported at the moment

  • Examples:

    • Example 1:

      • message = '123456 is my otp'
entity_name = 'regex_test_otp'
structured_value = None
fallback_value = None
bot_message = 'enter the otp'
regex = '\\d{4,6}'

      • Python:

        from ner_v1.chatbot.entity_detection import get_regex
output = get_regex(message=message,entity_name=entity_name, structured_value=structured_value,fallback_value=fallback_value,bot_message=bot_message, pattern=regex)
print output

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/regex/?message=123456%20is%20my%otp&entity_name=regex&structured_value=&fallback_value=&bot_message=enter%20the%otp%20&regex=\d{4,6}'

      • CURL Output:

        {
  "data": [
    {
      "detection": "message",
      "original_text": "123456",
      "entity_value": "123456"
    }
      ]
}

      ​

Data Tagging

  • This functionality tags the message with the entity name and also identifies the entity values.

  • Example:

    • Example 1:

      • entities = ['date','time','restaurant']
message = "Reserve me a table today at 6:30pm at Mainland China and on Monday at 7:00pm at Barbeque Nation"

      • CURL command:

        URL='localhost'
PORT=8081
        curl -i 'http://'$URL':'$PORT'/v1/ner/?entities=\[%22date%22,%22time%22,%22restaurant%22\]&message=Reserve%20me%20a%20table%20today%20at%206:30pm%20at%20Mainland%20China%20and%20on%20Monday%20at%207:00pm%20at%20Barbeque%20Nation'

      • CURL Output:

        {
  "data": {
    "tag": "reserve me a table __date__ at __time__ at __restaurant__ and on __date__ at __time__ at __restaurant__",
    "entity_data": {
      "date": [
        {
          "detection": "message",
          "original_text": "monday",
          "entity_value": {
            "mm": 3,
            "yy": 2017,
            "dd": 27,
            "type": "day_within_one_week"
          }
        },
        {
          "detection": "message",
          "original_text": "today",
          "entity_value": {
            "mm": 3,
            "yy": 2017,
            "dd": 21,
            "type": "today"
          }
        }
      ],
      "time": [
        {
          "detection": "message",
          "original_text": "6:30pm",
          "entity_value": {
            "mm": 30,
            "hh": 6,
            "nn": "pm"
          }
        },
        {
          "detection": "message",
          "original_text": "7:00pm",
          "entity_value": {
            "mm": 0,
            "hh": 7,
            "nn": "pm"
          }
        }
      ],
      "restaurant": [
        {
          "detection": "message",
          "original_text": "barbeque nation",
          "entity_value": {
            "value": "Barbeque Nation"
          }
        },
        {
          "detection": "message",
          "original_text": "mainland china",
          "entity_value": {
            "value": "Mainland China"
          }
        }
      ]
    }
  }
}