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 

1
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

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[
  "The Lion King",
  "The Nightmare Before Christmas",
  "The Godfather",
  "The Matrix",
  "Moulin Rouge!",
  "Indiana Jones",
  "The Greatest Showman",
  "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",
  "Moulin Rouge!",
  "Indiana Jones",
  "The Greatest Showman",
  "The Princess Bride",
  "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

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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

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[
    "The Lion King",
    "The Nightmare Before Christmas",
    "The Godfather",
    "The Matrix",
    "Up",
    "Indiana Jones",
    "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",
    "Up",
    "Indiana Jones",
    "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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9
['🎃', '👻', '🎄', '💀', '🕷️'],
['🔫', '🍝', '💼', '🚬'],
['💊', '🕶️', '💻', '🤖', '🔫'],
['❤️', '✈️', '🎹', '🇲🇦', '🕶️'],
['🗿', '🏺', '🦟', '🔫', '🌴'],
['🎭', '🔥', '🗡️', '📺', '🇬🇧'],
['👸', '🤴', '🗡️', '❤️', '👹'],
['🚗', '⏰', '⚡', '👟', '📖'],
['🏜️', '🌾', '🐛', '👑', '🚀']]
What are the names of the movie titles?

Output

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[
  "The Lion King",
  "The Nightmare Before Christmas",
  "Pulp Fiction",
  "The Matrix",
  "Casablanca",
  "Indiana Jones",
  "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",
  "Pulp Fiction",
  "The Matrix",
  "Casablanca",
  "Indiana Jones",
  "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

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[
  "The Lion King",
  "The Nightmare Before Christmas",
  "The Godfather",
  "The Matrix",
  "Aladdin",
  "Indiana Jones",
  "V for Vendetta",
  "The Lord of the Rings",
  "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",
  "Aladdin",
  "Indiana Jones",
  "V for Vendetta",
  "The Lord of the Rings",
  "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 
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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.

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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5
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7
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9
['🎃', '🎄', '👻', '🕸️', '💀'],
['🕴️', '💼', '🔫', '🍝', '🚬'],
['🕶️', '💊', '💻', '🤖', '🚪'],
['❤️', '✈️', '🎹', '🌆', '🇲🇦'],
['🧳', '⚔️', '🗺️', '🐍', '🏺'],
['🎭', '💣', '🔪', '🏛️', '📰'],
['👸', '🤴', '⚔️', '🧙', '❤️'],
['⏰', '🚗', '⚡', '👟', '📅'],
['🏜️', '🐛', '👑', '🌠', '🪐']]
What are the names of the movie titles?

Output

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[
  "The Lion King",
  "The Nightmare Before Christmas",
  "Pulp Fiction",
  "The Matrix",
  "Casablanca",
  "Indiana Jones",
  "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",
  "Pulp Fiction",
  "The Matrix",
  "Casablanca",
  "Indiana Jones",
  "V for Vendetta",
  "The Princess Bride",
  "Back to the Future",
  "Dune"
]

PyFunc

OK Resulting in output: True