Initial Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for 

1
you might might answer 
1
2
{"Finding Nemo": ["🐠", "🐟", "🐡", "🐬", "🐳"],
"Toy Story": ["🚀", "⚔️,", "🤖", "👽", "🌌"]}
. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies: 
1
Give ONLY a JSON output. Nothing else.

LLM Generation

Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for 

1
you might might answer 
1
2
{"Finding Nemo": ["🐠", "🐟", "🐡", "🐬", "🐳"],
"Toy Story": ["🚀", "⚔️,", "🤖", "👽", "🌌"]}
. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies: 
1
Give ONLY a JSON output. Nothing else.

Output

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
{
"The Lion King": ["🦁", "👑", "🌅", "🇰🇪", "🌱"],
"The Nightmare Before Christmas": ["🎃", "🎄", "👻", "🎶", " snowman"],
"The Godfather": ["💂‍♂️", "🔫", "🌇", "🍷", "🏦"],
"The Matrix": ["💊", "🖥️", "🤸‍♂️", "🕶️", "🌐"],
"Casablanca": ["🏙️", "🎩", "🕵️‍♂️", "💔", "🇲🇦"],
"Raiders of the Lost Ark": ["🏺", "🕰️", "🛖", "🗺️", "🐍"],
"V for Vendetta": ["🗡️", "💡", "👭", "🇬🇧", "🙈"],
"The Princess Bride": ["👸", "🤵", "🐭", "🗡️", "💘"],
"Back to the Future": ["🕰️", "🚀", "🚗", "⚡️", "🎸"],
"Dune": ["🏜️", "🪐", "🦑", "🗡️", "👑"]
}

Extract Json

I extracted the following JSON from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
{
"The Lion King": ["🦁", "👑", "🌅", "🇰🇪", "🌱"],
"The Nightmare Before Christmas": ["🎃", "🎄", "👻", "🎶", " snowman"],
"The Godfather": ["💂‍♂️", "🔫", "🌇", "🍷", "🏦"],
"The Matrix": ["💊", "🖥️", "🤸‍♂️", "🕶️", "🌐"],
"Casablanca": ["🏙️", "🎩", "🕵️‍♂️", "💔", "🇲🇦"],
"Raiders of the Lost Ark": ["🏺", "🕰️", "🛖", "🗺️", "🐍"],
"V for Vendetta": ["🗡️", "💡", "👭", "🇬🇧", "🙈"],
"The Princess Bride": ["👸", "🤵", "🐭", "🗡️", "💘"],
"Back to the Future": ["🕰️", "🚀", "🚗", "⚡️", "🎸"],
"Dune": ["🏜️", "🪐", "🦑", "🗡️", "👑"]
}

PyFunc

Resulting in output: [['🦁', '👑', '🌅', '🇰🇪', '🌱'], ['🎃', '🎄', '👻', '🎶'], ['🔫', '🌇', '🍷', '🏦'], ['💊', '🖥️', '🕶️', '🌐'], ['🏙️', '🎩', '💔', '🇲🇦'], ['🏺', '🕰️', '🛖', '🗺️', '🐍'], ['🗡️', '💡', '👭', '🇬🇧', '🙈'], ['👸', '🤵', '🐭', '🗡️', '💘'], ['🕰️', '🚀', '🚗', '⚡️', '🎸'], ['🏜️', '🪐', '🦑', '🗡️', '👑']]

LLM Generation

Query

For each of the following ten movies described by 5 emoji, give the movie title that best matches. Give your answer as a JSON list. So If I asked for 

1
You might answer 
1
["Finding Nemo", "Toy Story"]]
. Now give me answers for these movies: 
1
2
3
4
5
6
7
8
9
['🎃', '🎄', '👻', '🎶'],
['🔫', '🌇', '🍷', '🏦'],
['💊', '🖥️', '🕶️', '🌐'],
['🏙️', '🎩', '💔', '🇲🇦'],
['🏺', '🕰️', '🛖', '🗺️', '🐍'],
['🗡️', '💡', '👭', '🇬🇧', '🙈'],
['👸', '🤵', '🐭', '🗡️', '💘'],
['🕰️', '🚀', '🚗', '⚡️', '🎸'],
['🏜️', '🪐', '🦑', '🗡️', '👑']]
What are the names of the movie titles?

Output

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
[
  "The Lion King",
  "The Nightmare Before Christmas",
  "Inception",
  "The Matrix",
  "Casablanca",
  "Indiana Jones",
  "Sherlock Holmes",
  "Cinderella",
  "Back to the Future",
  "Dune"
]

Extract Json

I extracted the following JSON from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
[
  "The Lion King",
  "The Nightmare Before Christmas",
  "Inception",
  "The Matrix",
  "Casablanca",
  "Indiana Jones",
  "Sherlock Holmes",
  "Cinderella",
  "Back to the Future",
  "Dune"
]

PyFunc

OK Resulting in output: False

Initial Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for 

1
you might might answer 
1
2
{"Finding Nemo": ["🐠", "🐟", "🐡", "🐬", "🐳"],
"Toy Story": ["🚀", "⚔️,", "🤖", "👽", "🌌"]}
. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies: 
1
Give ONLY a JSON output. Nothing else.

LLM Generation

Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for 

1
you might might answer 
1
2
{"Finding Nemo": ["🐠", "🐟", "🐡", "🐬", "🐳"],
"Toy Story": ["🚀", "⚔️,", "🤖", "👽", "🌌"]}
. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies: 
1
Give ONLY a JSON output. Nothing else.

Output

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
{
"The Lion King": ["🦁", "👑", "🌿", "🇦🇫", "🌅"],
"The Nightmare Before Christmas": ["🎃", "🎄", "👻", "🎶", "☃️"],
"The Godfather": ["💼", "🔫", "🇮🇹", "👔", "🍷"],
"The Matrix": ["💊", "🖥️", "👓", "⚫️", "🗺️"],
"Casablanca": ["🏙️", "💔", "🌸", "🎩", "🎼"],
"Raiders of the Lost Ark": ["🏺", "🕶️", "🐍", "🛖", "🗺️"],
"V for Vendetta": ["🅱️", "🧢", "🗡️", "🔥", "🇬🇧"],
"The Princess Bride": ["👸", "🤵", "🐭", "🗡️", "💍"],
"Back to the Future": ["🕰️", "🚗", "🚀", "👨‍🔬", "⚡"],
"Dune": ["🏜️", "🐛", "👑", "🔮", "🗡️"]
}

Extract Json

I extracted the following JSON from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
{
"The Lion King": ["🦁", "👑", "🌿", "🇦🇫", "🌅"],
"The Nightmare Before Christmas": ["🎃", "🎄", "👻", "🎶", "☃️"],
"The Godfather": ["💼", "🔫", "🇮🇹", "👔", "🍷"],
"The Matrix": ["💊", "🖥️", "👓", "⚫️", "🗺️"],
"Casablanca": ["🏙️", "💔", "🌸", "🎩", "🎼"],
"Raiders of the Lost Ark": ["🏺", "🕶️", "🐍", "🛖", "🗺️"],
"V for Vendetta": ["🅱️", "🧢", "🗡️", "🔥", "🇬🇧"],
"The Princess Bride": ["👸", "🤵", "🐭", "🗡️", "💍"],
"Back to the Future": ["🕰️", "🚗", "🚀", "👨‍🔬", "⚡"],
"Dune": ["🏜️", "🐛", "👑", "🔮", "🗡️"]
}

PyFunc

Resulting in output: [['🦁', '👑', '🌿', '🇦🇫', '🌅'], ['🎃', '🎄', '👻', '🎶', '☃️'], ['💼', '🔫', '🇮🇹', '👔', '🍷'], ['💊', '🖥️', '👓', '⚫️', '🗺️'], ['🏙️', '💔', '🌸', '🎩', '🎼'], ['🏺', '🕶️', '🐍', '🛖', '🗺️'], ['🅱️', '🧢', '🗡️', '🔥', '🇬🇧'], ['👸', '🤵', '🐭', '🗡️', '💍'], ['🕰️', '🚗', '🚀', '⚡'], ['🏜️', '🐛', '👑', '🔮', '🗡️']]

LLM Generation

Query

For each of the following ten movies described by 5 emoji, give the movie title that best matches. Give your answer as a JSON list. So If I asked for 

1
You might answer 
1
["Finding Nemo", "Toy Story"]]
. Now give me answers for these movies: 
1
2
3
4
5
6
7
8
9
['🎃', '🎄', '👻', '🎶', '☃️'],
['💼', '🔫', '🇮🇹', '👔', '🍷'],
['💊', '🖥️', '👓', '⚫️', '🗺️'],
['🏙️', '💔', '🌸', '🎩', '🎼'],
['🏺', '🕶️', '🐍', '🛖', '🗺️'],
['🅱️', '🧢', '🗡️', '🔥', '🇬🇧'],
['👸', '🤵', '🐭', '🗡️', '💍'],
['🕰️', '🚗', '🚀', '⚡'],
['🏜️', '🐛', '👑', '🔮', '🗡️']]
What are the names of the movie titles?

Output

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
[
  "The Lion King",
  "The Nightmare Before Christmas",
  "The Godfather",
  "The Matrix",
  "La La Land",
  "Raiders of the Lost Ark",
  "Braveheart",
  "Cinderella",
  "Back to the Future",
  "Dune"
]

Extract Json

I extracted the following JSON from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
[
  "The Lion King",
  "The Nightmare Before Christmas",
  "The Godfather",
  "The Matrix",
  "La La Land",
  "Raiders of the Lost Ark",
  "Braveheart",
  "Cinderella",
  "Back to the Future",
  "Dune"
]

PyFunc

OK Resulting in output: False

Initial Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for 

1
you might might answer 
1
2
{"Finding Nemo": ["🐠", "🐟", "🐡", "🐬", "🐳"],
"Toy Story": ["🚀", "⚔️,", "🤖", "👽", "🌌"]}
. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies: 
1
Give ONLY a JSON output. Nothing else.

LLM Generation

Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for 

1
you might might answer 
1
2
{"Finding Nemo": ["🐠", "🐟", "🐡", "🐬", "🐳"],
"Toy Story": ["🚀", "⚔️,", "🤖", "👽", "🌌"]}
. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies: 
1
Give ONLY a JSON output. Nothing else.

Output

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
{
"The Lion King": ["🦁", "👑", "🌵", "🍃", "🌅"],
"The Nightmare Before Christmas": ["🎃", "🎄", "💀", "🎶", "🌙"],
"The Godfather": ["💔", "🔫", "🍷", "🏙️", "💰"],
"The Matrix": ["💊", "🕶️", "💻", "🤸", "🗺️"],
"Casablanca": ["💔", "🕯️", "🎹", "🕵️", "🛫"],
"Raiders of the Lost Ark": ["🏺", "🦏", "🕴️", "🐍", "🏜️"],
"V for Vendetta": ["👁️", "💥", "🗽", "🗳️", "🧪"],
"The Princess Bride": ["👸", "🤵", "🗡️", "🐀", "💘"],
"Back to the Future": ["🕰️", "🚀", "🚗", "🎸", "🌩️"],
"Dune": ["🏜️", "🐛", "👑", "🔪", "🌌"]
}

Extract Json

I extracted the following JSON from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
{
"The Lion King": ["🦁", "👑", "🌵", "🍃", "🌅"],
"The Nightmare Before Christmas": ["🎃", "🎄", "💀", "🎶", "🌙"],
"The Godfather": ["💔", "🔫", "🍷", "🏙️", "💰"],
"The Matrix": ["💊", "🕶️", "💻", "🤸", "🗺️"],
"Casablanca": ["💔", "🕯️", "🎹", "🕵️", "🛫"],
"Raiders of the Lost Ark": ["🏺", "🦏", "🕴️", "🐍", "🏜️"],
"V for Vendetta": ["👁️", "💥", "🗽", "🗳️", "🧪"],
"The Princess Bride": ["👸", "🤵", "🗡️", "🐀", "💘"],
"Back to the Future": ["🕰️", "🚀", "🚗", "🎸", "🌩️"],
"Dune": ["🏜️", "🐛", "👑", "🔪", "🌌"]
}

PyFunc

Resulting in output: [['🦁', '👑', '🌵', '🍃', '🌅'], ['🎃', '🎄', '💀', '🎶', '🌙'], ['💔', '🔫', '🍷', '🏙️', '💰'], ['💊', '🕶️', '💻', '🤸', '🗺️'], ['💔', '🕯️', '🎹', '🕵️', '🛫'], ['🏺', '🦏', '🕴️', '🐍', '🏜️'], ['👁️', '💥', '🗽', '🗳️', '🧪'], ['👸', '🤵', '🗡️', '🐀', '💘'], ['🕰️', '🚀', '🚗', '🎸', '🌩️'], ['🏜️', '🐛', '👑', '🔪', '🌌']]

LLM Generation

Query

For each of the following ten movies described by 5 emoji, give the movie title that best matches. Give your answer as a JSON list. So If I asked for 

1
You might answer 
1
["Finding Nemo", "Toy Story"]]
. Now give me answers for these movies: 
1
2
3
4
5
6
7
8
9
['🎃', '🎄', '💀', '🎶', '🌙'],
['💔', '🔫', '🍷', '🏙️', '💰'],
['💊', '🕶️', '💻', '🤸', '🗺️'],
['💔', '🕯️', '🎹', '🕵️', '🛫'],
['🏺', '🦏', '🕴️', '🐍', '🏜️'],
['👁️', '💥', '🗽', '🗳️', '🧪'],
['👸', '🤵', '🗡️', '🐀', '💘'],
['🕰️', '🚀', '🚗', '🎸', '🌩️'],
['🏜️', '🐛', '👑', '🔪', '🌌']]
What are the names of the movie titles?

Output

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
[
  "The Lion King",
  "The Nightmare Before Christmas",
  "La La Land",
  "Fight Club",
  "Casablanca",
  "Raiders of the Lost Ark",
  "V for Vendetta",
  "Ratatouille",
  "Back to the Future",
  "Dune"
]

Extract Json

I extracted the following JSON from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
[
  "The Lion King",
  "The Nightmare Before Christmas",
  "La La Land",
  "Fight Club",
  "Casablanca",
  "Raiders of the Lost Ark",
  "V for Vendetta",
  "Ratatouille",
  "Back to the Future",
  "Dune"
]

PyFunc

OK Resulting in output: False

Initial Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for 

1
you might might answer 
1
2
{"Finding Nemo": ["🐠", "🐟", "🐡", "🐬", "🐳"],
"Toy Story": ["🚀", "⚔️,", "🤖", "👽", "🌌"]}
. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies: 
1
Give ONLY a JSON output. Nothing else.

LLM Generation

Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for 

1
you might might answer 
1
2
{"Finding Nemo": ["🐠", "🐟", "🐡", "🐬", "🐳"],
"Toy Story": ["🚀", "⚔️,", "🤖", "👽", "🌌"]}
. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies: 
1
Give ONLY a JSON output. Nothing else.

Output

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
{"The Lion King": ["🦁", "👑", "🌵", "🏞️", "🌅"],
"The Nightmare Before Christmas": ["🎃", "🎄", "👻", "🎶", "🎷"],
"The Godfather": ["💂‍♂️", "🔫", "💰", "🏆", "🚬"],
"The Matrix": ["💊", "🕶️", "🤸‍♂️", "💻", "🗺️"],
"Casablanca": ["🕌", "🎩", "💔", "🎵", "🌙"],
"Raiders of the Lost Ark": ["🏺", "🗺️", "🐍", "🎩", "🚁"],
"V for Vendetta": ["💡", "🎭", "🗡️", "🔥", "🏛️"],
"The Princess Bride": ["👸", "🤵", "🐭", "🐍", "🏹"],
"Back to the Future": ["🗓️", "🚗", "🚀", "⏳", "🎸"],
"Dune": ["🌱", "🔪", "🏜️", "🐛", "🌟"]}

Extract Json

I extracted the following JSON from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
{"The Lion King": ["🦁", "👑", "🌵", "🏞️", "🌅"],
"The Nightmare Before Christmas": ["🎃", "🎄", "👻", "🎶", "🎷"],
"The Godfather": ["💂‍♂️", "🔫", "💰", "🏆", "🚬"],
"The Matrix": ["💊", "🕶️", "🤸‍♂️", "💻", "🗺️"],
"Casablanca": ["🕌", "🎩", "💔", "🎵", "🌙"],
"Raiders of the Lost Ark": ["🏺", "🗺️", "🐍", "🎩", "🚁"],
"V for Vendetta": ["💡", "🎭", "🗡️", "🔥", "🏛️"],
"The Princess Bride": ["👸", "🤵", "🐭", "🐍", "🏹"],
"Back to the Future": ["🗓️", "🚗", "🚀", "⏳", "🎸"],
"Dune": ["🌱", "🔪", "🏜️", "🐛", "🌟"]}

PyFunc

Resulting in output: [['🦁', '👑', '🌵', '🏞️', '🌅'], ['🎃', '🎄', '👻', '🎶', '🎷'], ['🔫', '💰', '🏆', '🚬'], ['💊', '🕶️', '💻', '🗺️'], ['🕌', '🎩', '💔', '🎵', '🌙'], ['🏺', '🗺️', '🐍', '🎩', '🚁'], ['💡', '🎭', '🗡️', '🔥', '🏛️'], ['👸', '🤵', '🐭', '🐍', '🏹'], ['🗓️', '🚗', '🚀', '⏳', '🎸'], ['🌱', '🔪', '🏜️', '🐛', '🌟']]

LLM Generation

Query

For each of the following ten movies described by 5 emoji, give the movie title that best matches. Give your answer as a JSON list. So If I asked for 

1
You might answer 
1
["Finding Nemo", "Toy Story"]]
. Now give me answers for these movies: 
1
2
3
4
5
6
7
8
9
['🎃', '🎄', '👻', '🎶', '🎷'],
['🔫', '💰', '🏆', '🚬'],
['💊', '🕶️', '💻', '🗺️'],
['🕌', '🎩', '💔', '🎵', '🌙'],
['🏺', '🗺️', '🐍', '🎩', '🚁'],
['💡', '🎭', '🗡️', '🔥', '🏛️'],
['👸', '🤵', '🐭', '🐍', '🏹'],
['🗓️', '🚗', '🚀', '⏳', '🎸'],
['🌱', '🔪', '🏜️', '🐛', '🌟']]
What are the names of the movie titles?

Output

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
[
  "The Lion King",
  "The Nightmare Before Christmas",
  "Casino Royale",
  "The Matrix",
  "Aladdin",
  "Indiana Jones",
  "The Mummy",
  "The Great Mouse Detective",
  "Back to the Future",
  "A Bug's Life"
]

Extract Json

I extracted the following JSON from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
[
  "The Lion King",
  "The Nightmare Before Christmas",
  "Casino Royale",
  "The Matrix",
  "Aladdin",
  "Indiana Jones",
  "The Mummy",
  "The Great Mouse Detective",
  "Back to the Future",
  "A Bug's Life"
]

PyFunc

OK Resulting in output: False

Initial Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for 

1
you might might answer 
1
2
{"Finding Nemo": ["🐠", "🐟", "🐡", "🐬", "🐳"],
"Toy Story": ["🚀", "⚔️,", "🤖", "👽", "🌌"]}
. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies: 
1
Give ONLY a JSON output. Nothing else.

LLM Generation

Query

For each of the following ten movies give at most 5 emoji that would best describe the movie. Give your answer as a JSON array. So If I asked for 

1
you might might answer 
1
2
{"Finding Nemo": ["🐠", "🐟", "🐡", "🐬", "🐳"],
"Toy Story": ["🚀", "⚔️,", "🤖", "👽", "🌌"]}
. Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1) Now give me answers for these movies: 
1
Give ONLY a JSON output. Nothing else.

Output

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
{"The Lion King": ["🦁", "👑", "🌵", "🇰🇪", "🌅"],
"The Nightmare Before Christmas": ["🎃", "🎄", "👻", "🎵", "🌙"],
"The Godfather": ["💼", "🔫", "🕴️", "🏺", "🇮🇹"],
"The Matrix": ["💊", "🖥️", "🤸", "🕶️", "🔺"],
"Casablanca": ["🌆", "🏙️", "💔", "🎩", "🇲🇦"],
"Raiders of the Lost Ark": ["🏺", "🦈", "🗺️", "🎩", "🚁"],
"V for Vendetta": ["👊", "💥", "🙍‍♂️", "😷", "🇬🇧"],
"The Princess Bride": ["👸", "🤵", "🐭", "🗡️", "💘"],
"Back to the Future": ["🕰️", "🚗", "🚀", "🕳️", "🎸"],
"Dune": ["🏜️", "🪐", "🐛", "🗡️", "👑"]}

Extract Json

I extracted the following JSON from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
{"The Lion King": ["🦁", "👑", "🌵", "🇰🇪", "🌅"],
"The Nightmare Before Christmas": ["🎃", "🎄", "👻", "🎵", "🌙"],
"The Godfather": ["💼", "🔫", "🕴️", "🏺", "🇮🇹"],
"The Matrix": ["💊", "🖥️", "🤸", "🕶️", "🔺"],
"Casablanca": ["🌆", "🏙️", "💔", "🎩", "🇲🇦"],
"Raiders of the Lost Ark": ["🏺", "🦈", "🗺️", "🎩", "🚁"],
"V for Vendetta": ["👊", "💥", "🙍‍♂️", "😷", "🇬🇧"],
"The Princess Bride": ["👸", "🤵", "🐭", "🗡️", "💘"],
"Back to the Future": ["🕰️", "🚗", "🚀", "🕳️", "🎸"],
"Dune": ["🏜️", "🪐", "🐛", "🗡️", "👑"]}

PyFunc

Resulting in output: [['🦁', '👑', '🌵', '🇰🇪', '🌅'], ['🎃', '🎄', '👻', '🎵', '🌙'], ['💼', '🔫', '🕴️', '🏺', '🇮🇹'], ['💊', '🖥️', '🤸', '🕶️', '🔺'], ['🌆', '🏙️', '💔', '🎩', '🇲🇦'], ['🏺', '🦈', '🗺️', '🎩', '🚁'], ['👊', '💥', '😷', '🇬🇧'], ['👸', '🤵', '🐭', '🗡️', '💘'], ['🕰️', '🚗', '🚀', '🕳️', '🎸'], ['🏜️', '🪐', '🐛', '🗡️', '👑']]

LLM Generation

Query

For each of the following ten movies described by 5 emoji, give the movie title that best matches. Give your answer as a JSON list. So If I asked for 

1
You might answer 
1
["Finding Nemo", "Toy Story"]]
. Now give me answers for these movies: 
1
2
3
4
5
6
7
8
9
['🎃', '🎄', '👻', '🎵', '🌙'],
['💼', '🔫', '🕴️', '🏺', '🇮🇹'],
['💊', '🖥️', '🤸', '🕶️', '🔺'],
['🌆', '🏙️', '💔', '🎩', '🇲🇦'],
['🏺', '🦈', '🗺️', '🎩', '🚁'],
['👊', '💥', '😷', '🇬🇧'],
['👸', '🤵', '🐭', '🗡️', '💘'],
['🕰️', '🚗', '🚀', '🕳️', '🎸'],
['🏜️', '🪐', '🐛', '🗡️', '👑']]
What are the names of the movie titles?

Output

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
[
  "The Lion King",
  "The Nightmare Before Christmas",
  "Jason Bourne",
  "Inception",
  "Casablanca",
  "Indiana Jones",
  "Kingsman: The Secret Service",
  "Cinderella",
  "Back to the Future",
  "Dune"
]

Extract Json

I extracted the following JSON from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
[
  "The Lion King",
  "The Nightmare Before Christmas",
  "Jason Bourne",
  "Inception",
  "Casablanca",
  "Indiana Jones",
  "Kingsman: The Secret Service",
  "Cinderella",
  "Back to the Future",
  "Dune"
]

PyFunc

OK Resulting in output: False