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 

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you might might answer 
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{"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 

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you might might answer 
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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

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{
  "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: 

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{
  "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: 
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['🎃', '🎄', '💀', '🌙', '🎭'],
['🎩', '🔫', '💍', '🇮🇹', '🍝'],
['💊', '🕶️', '🖥️', '🕴️', '🔫'],
['🎩', '🚬', '🍸', '✈️', '🎹'],
['🤠', '🐍', '💀', '💍', '🗿'],
['🎭', '🔪', '🇬🇧', '🖋️', '🎆'],
['👸', '⚔️', '😍', '🐀'],
['🚗', '⌚', '🎸', '🕰️', '📅'],
['🏜️', '🪱', '🔮', '🚀', '⚔️']]
What are the names of the movie titles?

Output

Here are my best guesses for the movie titles based on the emoji clues provided: 

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[
  "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: 

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[
  "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

OK Resulting in output: True

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

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{
  "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: 

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{
  "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: 
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3
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5
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['🎃', '🎄', '💀', '🕷️', '🌙'],
['🔫', '🎩', '💍', '🇮🇹', '🍝'],
['💊', '🕶️', '🖥️', '🥋', '🔫'],
['🎩', '💋', '✈️', '🎹', '🍸'],
['🤠', '🪨', '🐍', '💀', '🏺'],
['🎭', '💣', '🔪', '🇬🇧', '🖋️'],
['👸', '🗡️', '💍', '🏰', '❤️'],
['🚗', '⌚', '🎸', '📅', '⚡'],
['🏜️', '🐛', '🪱', '🚀', '💧']]
What are the names of the movie titles?

Output

Here are my guesses for the movie titles based on the emoji clues provided: 

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[
  "The Lion King",
  "The Nightmare Before Christmas",
  "The Godfather",
  "The Matrix",
  "Casablanca",
  "Indiana Jones and the Raiders of the Lost Ark",
  "Shakespeare in Love",
  "The Princess Bride",
  "Back to the Future",
  "Dune"
]

Extract Json

I extracted the following JSON from that output: 

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[
  "The Lion King",
  "The Nightmare Before Christmas",
  "The Godfather",
  "The Matrix",
  "Casablanca",
  "Indiana Jones and the Raiders of the Lost Ark",
  "Shakespeare in Love",
  "The Princess Bride",
  "Back to the Future",
  "Dune"
]

PyFunc

OK Resulting in output: True

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

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{
  "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: 

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{
  "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: 
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2
3
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9
['🎃', '🎄', '💀', '🌙', '🎅'],
['🔫', '🇮🇹', '💍', '🎩', '🏰'],
['💊', '🕶️', '🖥️', '🥋', '🔫'],
['🚬', '🍸', '🎹', '✈️', '💌'],
['🤠', '🪃', '🐍', '💀', '🗿'],
['🎭', '🌹', '💣', '🇬🇧', '⚔️'],
['👸', '🗡️', '🐀', '💍'],
['🚗', '⚡', '🎸', '👟', '📅'],
['🏜️', '🪱', '🔮', '🚀', '⚔️']]
What are the names of the movie titles?

Output

Here is my attempt at matching the emoji clues to movie titles: 

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[
  "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: 

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[
  "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

OK Resulting in output: True

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

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{
  "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: 

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{
  "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 

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You might answer 
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["Finding Nemo", "Toy Story"]]
. Now give me answers for these movies: 
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['💀', '🎃', '🎄', '🦴', '🎅'],
['🔫', '🇮🇹', '🖋️', '💍', '🎩'],
['💊', '🕶️', '🖥️', '🥄', '🔫'],
['💌', '🎹', '✈️', '🚬', '🍸'],
['🤠', '🪓', '💀', '🪨', '📜'],
['🎭', '🔪', '💣', '🇬🇧', '🎆'],
['👸', '⚔️', '💍', '🐀'],
['🚗', '⌚', '🎸', '📅', '⚡'],
['🏜️', '🐛', '🪱', '🚀', '💧']]
What are the names of the movie titles?

Output

Here are my guesses for the movie titles based on the emoji clues provided: 

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[
  "The Lion King",
  "The Nightmare Before Christmas",
  "The Godfather",
  "The Matrix",
  "Casablanca",
  "The Shining",
  "V for Vendetta",
  "The Princess Bride",
  "Back to the Future",
  "Dune"
]

Extract Json

I extracted the following JSON from that output: 

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[
  "The Lion King",
  "The Nightmare Before Christmas",
  "The Godfather",
  "The Matrix",
  "Casablanca",
  "The Shining",
  "V for Vendetta",
  "The Princess Bride",
  "Back to the Future",
  "Dune"
]

PyFunc

OK Resulting in output: True

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

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{
  "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: 

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{
  "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: 
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2
3
4
5
6
7
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9
['🎃', '🎄', '💀', '🌙', '🕷️'],
['🎩', '🔫', '💍', '🍝', '🐎'],
['💊', '🕶️', '🖥️', '🥋', '🔫'],
['🎩', '🍸', '✈️', '🎥', '❤️'],
['🤠', '🪨', '🐍', '👑', '💀'],
['🎭', '🗡️', '💣', '🌹', '🇬🇧'],
['👸', '⚔️', '😍', '🧙'],
['🚗', '⌚', '🎸', '📅'],
['🏜️', '🪱', '🔮', '👊', '🚀']]
What are the names of the movie titles?

Output

Here are my guesses for the movie titles based on the emoji clues you provided: 

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[
  "The Lion King",
  "The Nightmare Before Christmas",
  "The Godfather", 
  "The Matrix",
  "Catch Me If You Can",
  "Indiana Jones and the Temple of Doom",
  "V for Vendetta",
  "The Princess Bride",
  "Back to the Future",
  "Dune"
]

Extract Json

I extracted the following JSON from that output: 

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[
  "The Lion King",
  "The Nightmare Before Christmas",
  "The Godfather", 
  "The Matrix",
  "Catch Me If You Can",
  "Indiana Jones and the Temple of Doom",
  "V for Vendetta",
  "The Princess Bride",
  "Back to the Future",
  "Dune"
]

PyFunc

OK Resulting in output: True