What the full names, ids of each employee and the name of the country they are in?
SELECT T1.first_name, T1.last_name, T1.employee_id, T4.country_name FROM employees AS T1 JOIN departments AS T2 ON T1.department_id = T2.department_id JOIN locations AS T3 ON T2.location_id = T3.location_id JOIN countries AS T4 ON T3.country_id = T4.country_id
``` yaml
countries: # name of dataframe
- COUNTRY_ID:
    dtype: string
    sample_values: AR, AU, BE
- COUNTRY_NAME:
    dtype: string
    sample_values: Argentina, Australia, Belgium
- REGION_ID:
    dtype: int64
    sample_values: 2, 3, 1

departments: # name of dataframe
- DEPARTMENT_ID:
    dtype: int64
    sample_values: 10, 20, 30
- DEPARTMENT_NAME:
    dtype: string
    sample_values: Administration, Marketing, Purchasing
- MANAGER_ID:
    dtype: int64
    sample_values: 200, 201, 114
- LOCATION_ID:
    dtype: int64
    sample_values: 1700, 1800, 1700

employees: # name of dataframe
- EMPLOYEE_ID:
    dtype: int64
    sample_values: 100, 101, 102
- FIRST_NAME:
    dtype: string
    sample_values: Steven, Neena, Lex
- LAST_NAME:
    dtype: string
    sample_values: King, Kochhar, De Haan
- EMAIL:
    dtype: string
    sample_values: SKING, NKOCHHAR, LDEHAAN
- PHONE_NUMBER:
    dtype: string
    sample_values: 515.123.4567, 515.123.4568, 515.123.4569
- HIRE_DATE:
    dtype: datetime
    sample_values: 1987-06-17, 1987-06-18, 1987-06-19
- JOB_ID:
    dtype: string
    sample_values: AD_PRES, AD_VP, AD_VP
- SALARY:
    dtype: int64
    sample_values: 24000, 17000, 17000
- COMMISSION_PCT:
    dtype: float64
    sample_values: 0.0, 0.0, 0.0
- MANAGER_ID:
    dtype: int64
    sample_values: 0, 100, 100
- DEPARTMENT_ID:
    dtype: int64
    sample_values: 90, 90, 90

locations: # name of dataframe
- LOCATION_ID:
    dtype: int64
    sample_values: 1000, 1100, 1200
- STREET_ADDRESS:
    dtype: string
    sample_values: 1297 Via Cola di Rie, 93091 Calle della Testa, 2017 Shinjuku-ku
- POSTAL_CODE:
    dtype: numeric
    sample_values: 989, 10934, 1689
- CITY:
    dtype: string
    sample_values: Roma, Venice, Tokyo
- STATE_PROVINCE:
    dtype: string
    sample_values: ', , Tokyo Prefecture'
- COUNTRY_ID:
    dtype: string
    sample_values: IT, IT, JP

```
Foreign Key constraints:
Column 'department_id' in dataframe 'employees' has a foreign key relation to column 'department_id' in dataframe 'departments'.
Column 'country_id' in dataframe 'locations' has a foreign key relation to column 'country_id' in dataframe 'countries'.

FIRST_NAME,LAST_NAME,EMPLOYEE_ID,COUNTRY_NAME
Steven,King,100,United States of America
Neena,Kochhar,101,United States of America
Lex,De Haan,102,United States of America
Alexander,Hunold,103,United States of America
Bruce,Ernst,104,United States of America
David,Austin,105,United States of America
Valli,Pataballa,106,United States of America
Diana,Lorentz,107,United States of America
Nancy,Greenberg,108,United States of America
Daniel,Faviet,109,United States of America
John,Chen,110,United States of America
Ismael,Sciarra,111,United States of America
Jose Manuel,Urman,112,United States of America
Luis,Popp,113,United States of America
Den,Raphaely,114,United States of America
Alexander,Khoo,115,United States of America
Shelli,Baida,116,United States of America
Sigal,Tobias,117,United States of America
Guy,Himuro,118,United States of America
Karen,Colmenares,119,United States of America
Matthew,Weiss,120,United States of America
Adam,Fripp,121,United States of America
Payam,Kaufling,122,United States of America
Shanta,Vollman,123,United States of America
Kevin,Mourgos,124,United States of America
Julia,Nayer,125,United States of America
Irene,Mikkilineni,126,United States of America
James,Landry,127,United States of America
Steven,Markle,128,United States of America
Laura,Bissot,129,United States of America
Mozhe,Atkinson,130,United States of America
James,Marlow,131,United States of America
TJ,Olson,132,United States of America
Jason,Mallin,133,United States of America
Michael,Rogers,134,United States of America
Ki,Gee,135,United States of America
Hazel,Philtanker,136,United States of America
Renske,Ladwig,137,United States of America
Stephen,Stiles,138,United States of America
John,Seo,139,United States of America
Joshua,Patel,140,United States of America
Trenna,Rajs,141,United States of America
Curtis,Davies,142,United States of America
Randall,Matos,143,United States of America
Peter,Vargas,144,United States of America
Winston,Taylor,180,United States of America
Jean,Fleaur,181,United States of America
Martha,Sullivan,182,United States of America
Girard,Geoni,183,United States of America
Nandita,Sarchand,184,United States of America
Alexis,Bull,185,United States of America
Julia,Dellinger,186,United States of America
Anthony,Cabrio,187,United States of America
Kelly,Chung,188,United States of America
Jennifer,Dilly,189,United States of America
Timothy,Gates,190,United States of America
Randall,Perkins,191,United States of America
Sarah,Bell,192,United States of America
Britney,Everett,193,United States of America
Samuel,McCain,194,United States of America
Vance,Jones,195,United States of America
Alana,Walsh,196,United States of America
Kevin,Feeney,197,United States of America
Donald,OConnell,198,United States of America
Douglas,Grant,199,United States of America
Jennifer,Whalen,200,United States of America
Michael,Hartstein,201,Canada
Pat,Fay,202,Canada
Susan,Mavris,203,United Kingdom
Hermann,Baer,204,Germany
Shelley,Higgins,205,United States of America
William,Gietz,206,United States of America
