MMTU-benchmark/MMTU huggingface dataset:
Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0       |
|:------------|
| Alexandrite |
| Opal        |
| Diamond     |
| Amethyst    |
| Zircon      |
| Pearl       |
| Garnet      |
| Aquamarine  |
| Sapphire    |
| Emerald     |
| Tanzanite   |
| Citrine     |
| Ruby        |
| Topaz       |
| Turquoise   |
| Peridot     |
| Tourmaline  |

Input Table 2:
| Col_1     |
|:----------|
| June      |
| October   |
| December  |
| May       |
| September |
| March     |
| July      |
| January   |
| February  |
| April     |
| August    |
| November  |

Joined Table:



label: Garnet
January
Amethyst
February
Aquamarine
March
Diamond
April
Emerald
May
Pearl
June
Alexandrite
June
Ruby
July
Peridot
August
Sapphire
September
Tourmaline
October
Opal
October
Topaz
November
Citrine
November
Tanzanite
December
Zircon
December
Turquoise
December
Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0               |
|:--------------------|
| Larry Page          |
| David Koch          |
| Amancio Ortega      |
| Charles Koch        |
| Carlos Slim Helu    |
| Warren Buffett      |
| Jeff Bezos          |
| Stefan Persson      |
| Larry Ellison       |
| Li Ka-shing         |
| Jim Walton          |
| Christy Walton      |
| S. Robson Walton    |
| Michael Bloomberg   |
| Liliane Bettencourt |
| Sheldon Adelson     |
| Alice Walton        |
| Bernard Arnault     |
| Bill Gates          |
| Sergey Brin         |

Input Table 2:
| Col_1         |
|:--------------|
| Sweden        |
| Hong Kong     |
| United States |
| Mexico        |
| France        |
| Spain         |

Joined Table:



label: Bill Gates
United States
Carlos Slim Helu
Mexico
Amancio Ortega
Spain
Warren Buffett
United States
Larry Ellison
United States
Charles Koch
United States
David Koch
United States
Sheldon Adelson
United States
Christy Walton
United States
Jim Walton
United States
Liliane Bettencourt
France
Stefan Persson
Sweden
Alice Walton
United States
S. Robson Walton
United States
Bernard Arnault
France
Michael Bloomberg
United States
Larry Page
United States
Jeff Bezos
United States
Sergey Brin
United States
Li Ka-shing
Hong Kong
Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0                                |
|:-------------------------------------|
| State Grid Corporation of China      |
| Walmart                              |
| Royal Dutch Shell                    |
| Volkswagen                           |
| Sinopec                              |
| Toyota                               |
| Glencore                             |
| ExxonMobil                           |
| China National Petroleum Corporation |
| BP                                   |

Input Table 2:
| Col_1       |
|:------------|
| Retail      |
| Commodities |
| Automobiles |
| Petroleum   |
| Power       |

Joined Table:



label: Walmart
Retail
Royal Dutch Shell
Petroleum
Sinopec
Petroleum
China National Petroleum Corporation
Petroleum
ExxonMobil
Petroleum
BP
Petroleum
State Grid Corporation of China
Power
Volkswagen
Automobiles
Toyota
Automobiles
Glencore
Commodities
Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0               |
|:--------------------|
| Sony                |
| Google              |
| Dell                |
| Toshiba             |
| HP                  |
| Hitachi             |
| Apple Inc.          |
| Intel               |
| Samsung Electronics |
| LG Electronics      |
| IBM                 |
| Amazon.com          |
| Panasonic           |
| Microsoft           |
| Foxconn             |

Input Table 2:
| Col_1                  |
|:-----------------------|
| Mountain View, CA, USA |
| Palo Alto, CA, USA     |
| Tokyo, Japan           |
| Osaka, Japan           |
| Redmond, WA, USA       |
| Seoul, South Korea     |
| Austin, TX, USA        |
| Santa Clara, CA, USA   |
| New Taipei, Taiwan     |
| Armonk, NY, USA        |
| Cupertino, CA, USA     |
| Seattle, WA, USA       |

Joined Table:



label: Samsung Electronics
Seoul, South Korea
Apple Inc.
Cupertino, CA, USA
Foxconn
New Taipei, Taiwan
HP
Palo Alto, CA, USA
IBM
Armonk, NY, USA
Hitachi
Tokyo, Japan
Microsoft
Redmond, WA, USA
Amazon.com
Seattle, WA, USA
Sony
Tokyo, Japan
Panasonic
Osaka, Japan
Google
Mountain View, CA, USA
Dell
Austin, TX, USA
Toshiba
Tokyo, Japan
LG Electronics
Seoul, South Korea
Intel
Santa Clara, CA, USA
Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0                                |
|:-------------------------------------|
| State Grid Corporation of China      |
| Walmart                              |
| Royal Dutch Shell                    |
| Volkswagen                           |
| Sinopec                              |
| Toyota                               |
| Glencore                             |
| ExxonMobil                           |
| China National Petroleum Corporation |
| BP                                   |

Input Table 2:
| Col_1          |
|:---------------|
| Netherlands    |
| United States  |
| China          |
| Japan          |
| United Kingdom |
| Switzerland    |
| Germany        |

Joined Table:



label: Walmart
United States
Royal Dutch Shell
Netherlands
Sinopec
China
China National Petroleum Corporation
China
ExxonMobil
United States
BP
United Kingdom
State Grid Corporation of China
China
Volkswagen
Germany
Toyota
Japan
Glencore
Switzerland
Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0      |
|:-----------|
| Parnate    |
| Nardil     |
| Marplan    |
| Anafranil  |
| Elavil     |
| Norpramin  |
| Pamelor    |
| Sinequan   |
| Surmontil  |
| Celexa     |
| Lexapro    |
| Luvox      |
| Paxil      |
| Prozac     |
| Zoloft     |
| Cymbalta   |
| Effexor    |
| Remeron    |
| Wellbutrin |
| Desyrel    |

Input Table 2:
| Col_1           |
|:----------------|
| tranylcypromine |
| phenelzine      |
| isocarboxazid   |
| clomipramine    |
| amitriptyline   |
| desipramine     |
| nortriptyline   |
| doxepin         |
| trimipramine    |
| citalopram      |
| escitalopram    |
| fluvoxamine     |
| paroxetine      |
| fluoxetine      |
| sertraline      |
| duloxetine      |
| venlafaxine     |
| mirtazapine     |
| bupropion       |
| trazodone       |

Joined Table:



label: Parnate
tranylcypromine
Nardil
phenelzine
Marplan
isocarboxazid
Anafranil
clomipramine
Elavil
amitriptyline
Norpramin
desipramine
Pamelor
nortriptyline
Sinequan
doxepin
Surmontil
trimipramine
Celexa
citalopram
Lexapro
escitalopram
Luvox
fluvoxamine
Paxil
paroxetine
Prozac
fluoxetine
Zoloft
sertraline
Cymbalta
duloxetine
Effexor
venlafaxine
Remeron
mirtazapine
Wellbutrin
bupropion
Desyrel
trazodone
Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0                                   |
|:----------------------------------------|
| Mario Kart 7                            |
| New Super Mario Bros. 2                 |
| Monster Hunter 3 Ultimate               |
| Kid Icarus: Uprising                    |
| Dragon Warrior VII                      |
| The Legend of Zelda: Ocarina of Time 3D |
| Nintendogs + Cats                       |
| Luigi's Mansion: Dark Moon              |
| Super Mario 3D Land                     |
| Mario Tennis Open                       |
| Animal Crossing: New Leaf               |
| Paper Mario: Sticker Star               |
| Super Street Fighter IV: 3D Edition     |

Input Table 2:
| Col_1       |
|:------------|
| Nintendo    |
| Square Enix |
| Capcom      |

Joined Table:



label: Super Mario 3D Land
Nintendo
Mario Kart 7
Nintendo
New Super Mario Bros. 2
Nintendo
Animal Crossing: New Leaf
Nintendo
Nintendogs + Cats
Nintendo
The Legend of Zelda: Ocarina of Time 3D
Nintendo
Paper Mario: Sticker Star
Nintendo
Monster Hunter 3 Ultimate
Capcom
Luigi's Mansion: Dark Moon
Nintendo
Dragon Warrior VII
Square Enix
Kid Icarus: Uprising
Nintendo
Mario Tennis Open
Nintendo
Super Street Fighter IV: 3D Edition
Capcom
Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0                  |
|:-----------------------|
| Memphis Grizzlies      |
| Los Angeles Clippers   |
| San Antonio Spurs      |
| Boston Celtics         |
| Utah Jazz              |
| Milwaukee Bucks        |
| Charlotte Bobcats      |
| Los Angeles Lakers     |
| Sacramento Kings       |
| Indiana Pacers         |
| Oklahoma City Thunder  |
| Philadelphia 76ers     |
| Portland Trail Blazers |
| Miami Heat             |
| Brooklyn Nets          |
| New York Knicks        |
| Golden State Warriors  |
| Minnesota Timberwolves |
| Toronto Raptors        |
| Atlanta Hawks          |
| Houston Rockets        |
| Detroit Pistons        |
| Dallas Mavericks       |
| Denver Nuggets         |
| Cleveland Cavaliers    |
| Chicago Bulls          |
| New Orleans Hornets    |
| Washington Wizards     |
| Phoenix Suns           |
| Orlando Magic          |

Input Table 2:
| Col_1     |
|:----------|
| Southwest |
| Northwest |
| Pacific   |
| Central   |
| Southeast |
| Atlantic  |

Joined Table:



label: Boston Celtics
Atlantic
Brooklyn Nets
Atlantic
New York Knicks
Atlantic
Philadelphia 76ers
Atlantic
Toronto Raptors
Atlantic
Chicago Bulls
Central
Cleveland Cavaliers
Central
Detroit Pistons
Central
Indiana Pacers
Central
Milwaukee Bucks
Central
Atlanta Hawks
Southeast
Charlotte Bobcats
Southeast
Miami Heat
Southeast
Orlando Magic
Southeast
Washington Wizards
Southeast
Dallas Mavericks
Southwest
Houston Rockets
Southwest
Memphis Grizzlies
Southwest
New Orleans Hornets
Southwest
San Antonio Spurs
Southwest
Denver Nuggets
Northwest
Minnesota Timberwolves
Northwest
Portland Trail Blazers
Northwest
Oklahoma City Thunder
Northwest
Utah Jazz
Northwest
Golden State Warriors
Pacific
Los Angeles Clippers
Pacific
Los Angeles Lakers
Pacific
Phoenix Suns
Pacific
Sacramento Kings
Pacific
Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0       |
|:------------|
| Afghanistan |
| Bhutan      |
| Singapore   |
| Sri Lanka   |
| Sikkim      |
| England     |
| Malaysia    |
| India       |
| Bangladesh  |
| Maldives    |
| Iran        |
| Kashmir     |
| China       |
| Nepal       |

Input Table 2:
| Col_1            |
|:-----------------|
| Tudor rose       |
| Shapla           |
| Orchid           |
| Blue poppy       |
| Pink Rose        |
| Peony            |
| Tulip            |
| Chinese hibiscus |
| Noble orchid     |
| Water lily       |
| Lotus            |
| Rhododendron     |

Joined Table:



label: England
Tudor rose
Nepal
Rhododendron
Sikkim
Noble orchid
Sri Lanka
Water lily
Afghanistan
Tulip
Bangladesh
Shapla
Bhutan
Blue poppy
China
Peony
India
Lotus
Iran
Tulip
Kashmir
Rhododendron
Maldives
Pink Rose
Malaysia
Chinese hibiscus
Singapore
Orchid
Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0                |
|:---------------------|
| San Francisco 49ers  |
| Houston Texans       |
| Minnesota Vikings    |
| Oakland Raiders      |
| Philadelphia Eagles  |
| Arizona Cardinals    |
| Washington Redskins  |
| New York Giants      |
| San Diego Chargers   |
| Atlanta Falcons      |
| Kansas City Chiefs   |
| Buffalo Bills        |
| New Orleans Saints   |
| Jacksonville Jaguars |
| Tampa Bay Buccaneers |
| Baltimore Ravens     |
| Dallas Cowboys       |
| Green Bay Packers    |
| Cincinnati Bengals   |
| New York Jets        |
| Seattle Seahawks     |
| Pittsburgh Steelers  |
| Denver Broncos       |
| Carolina Panthers    |
| New England Patriots |
| St. Louis Rams       |
| Miami Dolphins       |
| Indianapolis Colts   |
| Cleveland Browns     |
| Chicago Bears        |
| Tennessee Titans     |
| Detroit Lions        |

Input Table 2:
| Col_1          |
|:---------------|
| North Carolina |
| New York       |
| Indiana        |
| Tennessee      |
| Minnesota      |
| California     |
| Florida        |
| New Jersey     |
| Washington     |
| Pennsylvania   |
| Michigan       |
| Texas          |
| Colorado       |
| Massachusetts  |
| Illinois       |
| Missouri       |
| Louisiana      |
| Ohio           |
| Georgia        |
| Maryland       |
| Wisconsin      |
| Arizona        |

Joined Table:



label: Washington Redskins
Maryland
New York Jets
New Jersey
New York Giants
New Jersey
Dallas Cowboys
Texas
Kansas City Chiefs
Missouri
Denver Broncos
Colorado
Miami Dolphins
Florida
Carolina Panthers
North Carolina
New Orleans Saints
Louisiana
Cleveland Browns
Ohio
Green Bay Packers
Wisconsin
Buffalo Bills
New York
Atlanta Falcons
Georgia
Houston Texans
Texas
Baltimore Ravens
Maryland
San Diego Chargers
California
San Francisco 49ers
California
Philadelphia Eagles
Pennsylvania
Tennessee Titans
Tennessee
New England Patriots
Massachusetts
Jacksonville Jaguars
Florida
Seattle Seahawks
Washington
St. Louis Rams
Missouri
Tampa Bay Buccaneers
Florida
Cincinnati Bengals
Ohio
Pittsburgh Steelers
Pennsylvania
Detroit Lions
Michigan
Minnesota Vikings
Minnesota
Arizona Cardinals
Arizona
Oakland Raiders
California
Indianapolis Colts
Indiana
Chicago Bears
Illinois
Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0            |
|:-----------------|
| Krakatoa         |
| Mount St. Helens |
| Mount Vesuvius   |
| Eyjafjallajokull |
| Nevado del Ruiz  |
| Mount Tambora    |
| Mauna Loa        |
| Mount Pinatubo   |

Input Table 2:
| Col_1         |
|:--------------|
| Colombia      |
| United States |
| Philippines   |
| Italy         |
| Iceland       |
| Indonesia     |

Joined Table:



label: Mount Vesuvius
Italy
Krakatoa
Indonesia
Mount St. Helens
United States
Mount Tambora
Indonesia
Mauna Loa
United States
Eyjafjallajokull
Iceland
Nevado del Ruiz
Colombia
Mount Pinatubo
Philippines
Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0     |
|:----------|
| Miranda   |
| Tethys    |
| Moon      |
| Io        |
| Enceladus |
| Mimas     |

Input Table 2:
| Col_1   |
|:--------|
| Earth   |
| Jupiter |
| Saturn  |
| Uranus  |

Joined Table:



label: Mimas
Saturn
Enceladus
Saturn
Miranda
Uranus
Tethys
Saturn
Io
Jupiter
Moon
Earth
Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0       |
|:------------|
| Brampton    |
| Winnipeg    |
| Vancouver   |
| Calgary     |
| Edmonton    |
| Mississauga |
| Hamilton    |
| Ottawa      |
| Montreal    |
| Toronto     |

Input Table 2:
| Col_1            |
|:-----------------|
| Manitoba         |
| Alberta          |
| Quebec           |
| British Columbia |
| Ontario          |

Joined Table:



label: Toronto
Ontario
Montreal
Quebec
Calgary
Alberta
Ottawa
Ontario
Edmonton
Alberta
Mississauga
Ontario
Winnipeg
Manitoba
Vancouver
British Columbia
Brampton
Ontario
Hamilton
Ontario
Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0               |
|:--------------------|
| Larry Page          |
| David Koch          |
| Amancio Ortega      |
| Charles Koch        |
| Carlos Slim Helu    |
| Warren Buffett      |
| Jeff Bezos          |
| Stefan Persson      |
| Larry Ellison       |
| Li Ka-shing         |
| Jim Walton          |
| Christy Walton      |
| S. Robson Walton    |
| Michael Bloomberg   |
| Liliane Bettencourt |
| Sheldon Adelson     |
| Alice Walton        |
| Bernard Arnault     |
| Bill Gates          |
| Sergey Brin         |

Input Table 2:
| Col_1              |
|:-------------------|
| Google             |
| L’Oreal            |
| LVMH               |
| Diversified        |
| H&M                |
| Telecom            |
| Retail             |
| Walmart            |
| Casinos            |
| Bloomberg LP       |
| Microsoft          |
| Oracle             |
| Amazon             |
| Berkshire Hathaway |

Joined Table:



label: Bill Gates
Microsoft
Carlos Slim Helu
Telecom
Amancio Ortega
Retail
Warren Buffett
Berkshire Hathaway
Larry Ellison
Oracle
Charles Koch
Diversified
David Koch
Diversified
Sheldon Adelson
Casinos
Christy Walton
Walmart
Jim Walton
Walmart
Liliane Bettencourt
L’Oreal
Stefan Persson
H&M
Alice Walton
Walmart
S. Robson Walton
Walmart
Bernard Arnault
LVMH
Michael Bloomberg
Bloomberg LP
Larry Page
Google
Jeff Bezos
Amazon
Sergey Brin
Google
Li Ka-shing
Diversified
Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0          |
|:---------------|
| North Carolina |
| Indiana        |
| Wyoming        |
| Utah           |
| Arizona        |
| Montana        |
| Kentucky       |
| California     |
| Kansas         |
| Delaware       |
| Florida        |
| Pennsylvania   |
| Iowa           |
| Mississippi    |
| Illinois       |
| Texas          |
| Connecticut    |
| Georgia        |
| Maryland       |
| Virginia       |
| Idaho          |
| Oregon         |
| Vermont        |
| Maine          |
| Oklahoma       |
| Tennessee      |
| Alabama        |
| Arkansas       |
| South Carolina |
| Washington     |
| Nebraska       |
| West Virginia  |
| Colorado       |
| Massachusetts  |
| Missouri       |
| Alaska         |
| North Dakota   |
| Wisconsin      |
| Nevada         |
| New York       |
| Rhode Island   |
| Hawaii         |
| South Dakota   |
| Minnesota      |
| New Jersey     |
| Michigan       |
| New Mexico     |
| New Hampshire  |
| Louisiana      |
| Ohio           |

Input Table 2:
| Col_1   |
|:--------|
| DE      |
| HI      |
| TX      |
| MA      |
| MD      |
| IA      |
| ME      |
| ID      |
| MI      |
| UT      |
| MN      |
| MO      |
| IL      |
| IN      |
| MS      |
| MT      |
| AK      |
| AL      |
| VA      |
| AR      |
| NC      |
| ND      |
| NE      |
| RI      |
| AZ      |
| NH      |
| NJ      |
| VT      |
| NM      |
| FL      |
| NV      |
| WA      |
| NY      |
| SC      |
| SD      |
| WI      |
| OH      |
| GA      |
| OK      |
| CA      |
| WV      |
| WY      |
| OR      |
| KS      |
| CO      |
| KY      |
| CT      |
| PA      |
| LA      |
| TN      |

Joined Table:



label: Alabama
AL
Alaska
AK
Arizona
AZ
Arkansas
AR
California
CA
Colorado
CO
Connecticut
CT
Delaware
DE
Florida
FL
Georgia
GA
Hawaii
HI
Idaho
ID
Illinois
IL
Indiana
IN
Iowa
IA
Kansas
KS
Kentucky
KY
Louisiana
LA
Maine
ME
Maryland
MD
Massachusetts
MA
Michigan
MI
Minnesota
MN
Mississippi
MS
Missouri
MO
Montana
MT
Nebraska
NE
Nevada
NV
New Hampshire
NH
New Jersey
NJ
New Mexico
NM
New York
NY
North Carolina
NC
North Dakota
ND
Ohio
OH
Oklahoma
OK
Oregon
OR
Pennsylvania
PA
Rhode Island
RI
South Carolina
SC
South Dakota
SD
Tennessee
TN
Texas
TX
Utah
UT
Vermont
VT
Virginia
VA
Washington
WA
West Virginia
WV
Wisconsin
WI
Wyoming
WY
Task: semantic-join (source dataset: DataXFormer)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0   |
|:--------|
| #       |
| (       |
| 3       |
| C       |
| L       |
| 2       |
| x       |
| {       |
| +       |

Input Table 2:
|   Col_1 |
|--------:|
|      35 |
|      40 |
|      51 |
|      67 |
|      76 |
|      50 |
|     120 |
|     123 |
|      43 |

Joined Table:



label: #
35
(
40
3
51
C
67
L
76
2
50
x
120
{
123
+
43
Task: semantic-join (source dataset: DataXFormer)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0             |
|:------------------|
| 128.31.34.154     |
| 130.0.75.55       |
| 155.56.47.116     |
| 129.42.38.1       |
| 137.254.120.50    |
| 66.220.158.68     |
| 978-1-118-12938-8 |
| 140.205.248.2     |
| 162.13.40.196     |
| 191.239.213.197   |

Input Table 2:
| Col_1          |
|:---------------|
| United States  |
| Germany        |
| Germany        |
| United States  |
| United States  |
| United States  |
| China          |
| Costa Rica     |
| United Kingdom |
| United States  |

Joined Table:



label: 128.31.34.154
United States
130.0.75.55
Germany
155.56.47.116
Germany
129.42.38.1
United States
137.254.120.50
United States
66.220.158.68
United States
978-1-118-12938-8
China
140.205.248.2
Costa Rica
162.13.40.196
United Kingdom
191.239.213.197
United States
Task: semantic-join (source dataset: DataXFormer)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0                           |
|:--------------------------------|
| Amazon.com Inc.                 |
| Google Inc                      |
| International Business Machines |
| Bank of America Corporation     |
| General Electric Company        |
| General Motors Company          |
| Comcast Corporation             |
| Apple Inc                       |
| Microsoft Corporation           |
| SAP AG                          |

Input Table 2:
| Col_1        |
|:-------------|
| BBG00094DJF9 |
| BBG0013YWBF3 |
| BBG0000WHK50 |
| BBG00000YH03 |
| BBG0000112Y3 |
| BBG009KHTL39 |
| BBG00001KTF4 |
| BBG004HSPP80 |
| BBG00009KTG6 |
| BBG000BR9Q00 |

Joined Table:



label: Amazon.com Inc.
BBG00094DJF9
Google Inc
BBG0013YWBF3
International Business Machines
BBG0000WHK50
Bank of America Corporation
BBG00000YH03
General Electric Company
BBG0000112Y3
General Motors Company
BBG009KHTL39
Comcast Corporation
BBG00001KTF4
Apple Inc
BBG004HSPP80
Microsoft Corporation
BBG00009KTG6
SAP AG
BBG000BR9Q00
Task: semantic-join (source dataset: DataXFormer)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0             |
|:------------------|
| 0-553-10354-7     |
| 1501260049        |
| 0-688-03036-X     |
| 978-0374275631    |
| 033036426X        |
| 9.78055E+12       |
| 978-1-118-12938-8 |
| 978-3-642-29575-1 |
| 978-0451476807    |
| 978-0312427986    |

Input Table 2:
| Col_1                           |
|:--------------------------------|
| A Game of thrones               |
| The innovator's dilemma         |
| The Cider House Rules           |
| Thinking Fast and Slow          |
| The book Thief                  |
| The google Story                |
| Operating system concepts       |
| In-Memory Data Management       |
| Eye of the needle               |
| The war of the end of the world |

Joined Table:



label: 0-553-10354-7
A Game of thrones
1501260049
The innovator's dilemma
0-688-03036-X
The Cider House Rules
978-0374275631
Thinking Fast and Slow
033036426X
The book Thief
9.78055E+12
The google Story
978-1-118-12938-8
Operating system concepts
978-3-642-29575-1
In-Memory Data Management
978-0451476807
Eye of the needle
978-0312427986
The war of the end of the world
Task: semantic-join (source dataset: DataXFormer)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0       |
|:------------|
| Rotterdam   |
| Marseille   |
| Toronto     |
| Istanbul    |
| Aarhus      |
| Munich      |
| Barcelona   |
| Alexandria  |
| Los Angeles |
| Krakow      |

Input Table 2:
| Col_1                    |
|:-------------------------|
| 51.924420/4.477733       |
| 43.296482/5.369780       |
| 43.653226/-79.383184     |
| 41.008238/28.978359      |
| 56.162939/10.203921      |
| 48.135125/11.581981      |
| 41.385064/2.173403       |
| (38.804836/ -77.046921)  |
| (34.052234/ -118.243685) |
| (50.064650/ 19.94498)    |

Joined Table:



label: Rotterdam
51.924420/4.477733
Marseille
43.296482/5.369780
Toronto
43.653226/-79.383184
Istanbul
41.008238/28.978359
Aarhus
56.162939/10.203921
Munich
48.135125/11.581981
Barcelona
41.385064/2.173403
Alexandria
(38.804836/ -77.046921)
Los Angeles
(34.052234/ -118.243685)
Krakow
(50.064650/ 19.94498)
Task: semantic-join (source dataset: DataXFormer)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
|   Col_0 |
|--------:|
|     6   |
|     6.5 |
|     7   |
|     7.5 |
|     8   |
|     8.5 |
|     9   |
|     9.5 |
|    10   |
|    10.5 |
|    11   |
|    11.5 |
|    12   |
|    12.5 |
|    13   |

Input Table 2:
|   Col_1 |
|--------:|
|    38   |
|    38.7 |
|    39.3 |
|    40   |
|    40.5 |
|    41   |
|    42   |
|    42.5 |
|    43   |
|    44   |
|    44.5 |
|    45   |
|    46   |
|    46.5 |
|    47   |

Joined Table:



label: 6
38
6.5
38.7
7
39.3
7.5
40
8
40.5
8.5
41
9
42
9.5
42.5
10
43
10.5
44
11
44.5
11.5
45
12
46
12.5
46.5
13
47
Task: semantic-join (source dataset: DataXFormer)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
|   Col_0 |
|--------:|
|      32 |
|      77 |
|      30 |
|      41 |
|      59 |
|      68 |
|     212 |
|      50 |
|      86 |

Input Table 2:
|   Col_1 |
|--------:|
|    0    |
|   25    |
|   -1.11 |
|    5    |
|   15    |
|   20    |
|  100    |
|   10    |
|   30    |

Joined Table:



label: 32
0
77
25
30
-1.11
41
5
59
15
68
20
212
100
50
10
86
30
Task: semantic-join (source dataset: DataXFormer)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
|   Col_0 |
|--------:|
|       1 |
|      70 |
|      99 |
|       8 |
|      59 |
|      68 |
|     212 |
|     192 |
|     190 |

Input Table 2:
|   Col_1 |
|--------:|
|    0.45 |
|   31.75 |
|   44.9  |
|    3.6  |
|   26.76 |
|   30.84 |
|   96.16 |
|   87.09 |
|   86.18 |

Joined Table:



label: 1
0.45
70
31.75
99
44.9
8
3.6
59
26.76
68
30.84
212
96.16
192
87.09
190
86.18
Task: semantic-join (source dataset: DataXFormer)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0   |
|:--------|
| AMZN    |
| GOOGL   |
| IBM     |
| BAC     |
| GE      |
| GM      |
| CMCSA   |
| AAPL    |
| MSFT    |
| SAP     |

Input Table 2:
| Col_1                           |
|:--------------------------------|
| Amazon.com Inc.                 |
| Google Inc                      |
| International Business Machines |
| Bank of America Corporation     |
| General Electric Company        |
| General Motors Company          |
| Comcast Corporation             |
| Apple Inc                       |
| Microsoft Corporation           |
| SAP AG O.N.                     |

Joined Table:



label: AMZN
Amazon.com Inc.
GOOGL
Google Inc
IBM
International Business Machines
BAC
Bank of America Corporation
GE
General Electric Company
GM
General Motors Company
CMCSA
Comcast Corporation
AAPL
Apple Inc
MSFT
Microsoft Corporation
SAP
SAP AG O.N.
Task: semantic-join (source dataset: DataXFormer)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0           |
|:----------------|
| Oracle          |
| IBM             |
| Microsoft       |
| GM              |
| GE              |
| Bank of America |
| Staples         |
| Starbucks       |
| Nokia           |
| Dell            |

Input Table 2:
| Col_1             |
|:------------------|
| Larry Ellison     |
| Ginni Rometty     |
| Satya Nadella     |
| Mary T. Barra     |
| Jeffrey R. Immelt |
| Brian T. Moynihan |
| Ron Sargent       |
| Howard Schultz    |
| Rajeev Suri       |
| Michael S. Dell   |

Joined Table:



label: Oracle
Larry Ellison
IBM
Ginni Rometty
Microsoft
Satya Nadella
GM
Mary T. Barra
GE
Jeffrey R. Immelt
Bank of America
Brian T. Moynihan
Staples
Ron Sargent
Starbucks
Howard Schultz
Nokia
Rajeev Suri
Dell
Michael S. Dell
Task: semantic-join (source dataset: DataXFormer)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
|   Col_0 |
|--------:|
|   75657 |
|   76825 |
|   45052 |
|    2139 |
|    2215 |
|   45055 |
|   45050 |
|   45051 |
|   61880 |
|   96143 |

Input Table 2:
| Col_1   |
|:--------|
| TX      |
| TX      |
| OH      |
| MA      |
| MA      |
| OH      |
| OH      |
| OH      |
| IL      |
| CA      |

Joined Table:



label: 75657
TX
76825
TX
45052
OH
2139
MA
2215
MA
45055
OH
45050
OH
45051
OH
61880
IL
96143
CA
Task: semantic-join (source dataset: DataXFormer)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0                  |
|:-----------------------|
| Salamanders            |
| Albatross              |
| Anhingas               |
| Barn Owls              |
| Chickadees and Titmice |
| Chickadees and Titmice |
| Cormorants             |
| Cormorants             |
| Cormorants             |
| Cormorants             |
| Cormorants             |
| Cormorants             |
| Cranes                 |
| Falcons                |
| Falcons                |
| Flycatchers            |
| Frigatebirds           |
| Grebes                 |
| Grebes                 |
| Grebes                 |
| Grebes                 |
| Grebes                 |
| Grebes                 |

Input Table 2:
| Col_1                    |
|:-------------------------|
| Spotted Salamander       |
| Laysan Albatross         |
| Anhinga                  |
| Barn Owl                 |
| Black-Capped Chickadee   |
| Carolina Chickadee       |
| Double-Crested Cormorant |
| Neotropic Cormorant      |
| Great Cormorant          |
| Pelagic Cormorant        |
| Cormorant                |
| Red-Faced Cormorant      |
| Sandhill Crane           |
| Merlin                   |
| Gyrfalcon                |
| Eastern Phoebe           |
| Great Frigatebird        |
| Clark's Grebe            |
| Western Grebe            |
| Horned Grebe             |
| Red-Necked Grebe         |
| Eared Grebe              |
| Pied-Billed Grebe        |

Joined Table:



label: Salamanders
Spotted Salamander
Albatross
Laysan Albatross
Anhingas
Anhinga
Barn Owls
Barn Owl
Chickadees and Titmice
Black-Capped Chickadee
Chickadees and Titmice
Carolina Chickadee
Cormorants
Double-Crested Cormorant
Cormorants
Neotropic Cormorant
Cormorants
Great Cormorant
Cormorants
Pelagic Cormorant
Cormorants
Cormorant
Cormorants
Red-Faced Cormorant
Cranes
Sandhill Crane
Falcons
Merlin
Falcons
Gyrfalcon
Flycatchers
Eastern Phoebe
Frigatebirds
Great Frigatebird
Grebes
Clark's Grebe
Grebes
Western Grebe
Grebes
Horned Grebe
Grebes
Red-Necked Grebe
Grebes
Eared Grebe
Grebes
Pied-Billed Grebe