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
you might might answer
| {"Finding Nemo": ["ðŸ ", "ðŸŸ", "ðŸ¡", "ðŸ¬", "ðŸ³"],
"Toy Story": ["🚀", "âš”ï¸,", "🤖", "👽", "🌌"]}
|
.
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:
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
you might might answer
| {"Finding Nemo": ["ðŸ ", "ðŸŸ", "ðŸ¡", "ðŸ¬", "ðŸ³"],
"Toy Story": ["🚀", "âš”ï¸,", "🤖", "👽", "🌌"]}
|
.
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:
Give ONLY a JSON output. Nothing else.
Output
{
"The Lion King": ["ðŸ¦", "👑", "💔", "🌅", "ðŸ’"],
"The Nightmare Before Christmas": ["💀", "🎃", "👻", "â„ï¸", "🎄"],
"The Godfather": ["🕴ï¸", "💼", "🔫", "👨", "💰"],
"The Matrix": ["🤖", "âš¡", "🕶ï¸", "💊", "ðŸŒ"],
"Casablanca": ["💕", "🚬", "🎶", "🌃", "✈ï¸"],
"Raiders of the Lost Ark": ["ðŸº", "ðŸ", "🗺ï¸", "ðŸ”", "🤠"],
"V for Vendetta": ["ðŸŽ", "🔥", "✊", "🗽", "🕊ï¸"],
"The Princess Bride": ["â¤ï¸", "🗡ï¸", "👸", "ðŸ’", "ðŸŽ"],
"Back to the Future": ["â°", "🚗", "🔬", "👨", "🕰ï¸"],
"Dune": ["ðŸœï¸", "ðŸ›", "🚀", "👑", "âš”ï¸"]
}
Extract Json
I extracted the following JSON from that output:
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12 | {
"The Lion King": ["ðŸ¦", "👑", "💔", "🌅", "ðŸ’"],
"The Nightmare Before Christmas": ["💀", "🎃", "👻", "â„ï¸", "🎄"],
"The Godfather": ["🕴ï¸", "💼", "🔫", "👨", "💰"],
"The Matrix": ["🤖", "âš¡", "🕶ï¸", "💊", "ðŸŒ"],
"Casablanca": ["💕", "🚬", "🎶", "🌃", "✈ï¸"],
"Raiders of the Lost Ark": ["ðŸº", "ðŸ", "🗺ï¸", "ðŸ”", "🤠"],
"V for Vendetta": ["ðŸŽ", "🔥", "✊", "🗽", "🕊ï¸"],
"The Princess Bride": ["â¤ï¸", "🗡ï¸", "👸", "ðŸ’", "ðŸŽ"],
"Back to the Future": ["â°", "🚗", "🔬", "👨", "🕰ï¸"],
"Dune": ["ðŸœï¸", "ðŸ›", "🚀", "👑", "âš”ï¸"]
}
|
PyFunc
Resulting in output:
[['ðŸ¦', '👑', '💔', '🌅', 'ðŸ’'],
['💀', '🎃', '👻', 'â„ï¸', '🎄'],
['🕴ï¸', '💼', '🔫', '👨', '💰'],
['🤖', 'âš¡', '🕶ï¸', '💊', 'ðŸŒ'],
['💕', '🚬', '🎶', '🌃', '✈ï¸'],
['ðŸº', 'ðŸ', '🗺ï¸', 'ðŸ”', '🤠'],
['ðŸŽ', '🔥', '✊', '🗽', '🕊ï¸'],
['â¤ï¸', '🗡ï¸', '👸', 'ðŸ’', 'ðŸŽ'],
['â°', '🚗', '🔬', '👨', '🕰ï¸'],
['ðŸœï¸', 'ðŸ›', '🚀', '👑', 'âš”ï¸']]
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
You might answer
| ["Finding Nemo", "Toy Story"]]
|
.
Now give me answers for these movies:
| ['💀', '🎃', '👻', 'â„ï¸', '🎄'],
['🕴ï¸', '💼', '🔫', '👨', '💰'],
['🤖', 'âš¡', '🕶ï¸', '💊', 'ðŸŒ'],
['💕', '🚬', '🎶', '🌃', '✈ï¸'],
['ðŸº', 'ðŸ', '🗺ï¸', 'ðŸ”', '🤠'],
['ðŸŽ', '🔥', '✊', '🗽', '🕊ï¸'],
['â¤ï¸', '🗡ï¸', '👸', 'ðŸ’', 'ðŸŽ'],
['â°', '🚗', '🔬', '👨', '🕰ï¸'],
['ðŸœï¸', 'ðŸ›', '🚀', '👑', 'âš”ï¸']]
|
What are the names of the movie titles?
Output
Here's the JSON list with the movie titles:
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12 | [
"The Lion King",
"A Nightmare Before Christmas",
"Pulp Fiction",
"The Matrix",
"La La Land",
"Indiana Jones and the Raiders of the Lost Ark",
"Hamilton",
"Tristan and Isolde",
"Back to the Future",
"Dune"
]
|
Each movie title corresponds to the emoji sequence in the order they were presented. I interpreted the emojis based on their symbolic representation of key themes, characters, or elements from each movie.
Extract Json
I extracted the following JSON from that output:
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12 | [
"The Lion King",
"A Nightmare Before Christmas",
"Pulp Fiction",
"The Matrix",
"La La Land",
"Indiana Jones and the Raiders of the Lost Ark",
"Hamilton",
"Tristan and Isolde",
"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
you might might answer
| {"Finding Nemo": ["ðŸ ", "ðŸŸ", "ðŸ¡", "ðŸ¬", "ðŸ³"],
"Toy Story": ["🚀", "âš”ï¸,", "🤖", "👽", "🌌"]}
|
.
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:
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
you might might answer
| {"Finding Nemo": ["ðŸ ", "ðŸŸ", "ðŸ¡", "ðŸ¬", "ðŸ³"],
"Toy Story": ["🚀", "âš”ï¸,", "🤖", "👽", "🌌"]}
|
.
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:
Give ONLY a JSON output. Nothing else.
Output
{
"The Lion King": ["ðŸ¦", "👑", "ðŸŒ", "💔", "â¤ï¸"],
"The Nightmare Before Christmas": ["💀", "🎃", "👻", "â„ï¸", "🎄"],
"The Godfather": ["🔫", "🕴ï¸", "💼", "🇮🇹", "🚬"],
"The Matrix": ["💊", "🤖", "👨â€ðŸ’»", "ðŸŒ", "🕶ï¸"],
"Casablanca": ["💘", "🚬", "ðŸ¸", "✈ï¸", "🇲🇦"],
"Raiders of the Lost Ark": ["ðŸº", "ðŸ", "🌄", "ðŸ”", "🤠"],
"V for Vendetta": ["ðŸŽ", "🔥", "🥷", "🇬🇧", "âš”ï¸"],
"The Princess Bride": ["💕", "âš”ï¸", "ðŸŽ", "👸", "ðŸ€"],
"Back to the Future": ["🚗", "â°", "🔬", "💥", "👨â€ðŸ‘©â€ðŸ‘¦"],
"Dune": ["ðŸœï¸", "ðŸ›", "🚀", "👑", "💧"]
}
Extract Json
I extracted the following JSON from that output:
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12 | {
"The Lion King": ["ðŸ¦", "👑", "ðŸŒ", "💔", "â¤ï¸"],
"The Nightmare Before Christmas": ["💀", "🎃", "👻", "â„ï¸", "🎄"],
"The Godfather": ["🔫", "🕴ï¸", "💼", "🇮🇹", "🚬"],
"The Matrix": ["💊", "🤖", "👨â€ðŸ’»", "ðŸŒ", "🕶ï¸"],
"Casablanca": ["💘", "🚬", "ðŸ¸", "✈ï¸", "🇲🇦"],
"Raiders of the Lost Ark": ["ðŸº", "ðŸ", "🌄", "ðŸ”", "🤠"],
"V for Vendetta": ["ðŸŽ", "🔥", "🥷", "🇬🇧", "âš”ï¸"],
"The Princess Bride": ["💕", "âš”ï¸", "ðŸŽ", "👸", "ðŸ€"],
"Back to the Future": ["🚗", "â°", "🔬", "💥", "👨â€ðŸ‘©â€ðŸ‘¦"],
"Dune": ["ðŸœï¸", "ðŸ›", "🚀", "👑", "💧"]
}
|
PyFunc
Resulting in output:
[['ðŸ¦', '👑', 'ðŸŒ', '💔', 'â¤ï¸'],
['💀', '🎃', '👻', 'â„ï¸', '🎄'],
['🔫', '🕴ï¸', '💼', '🇮🇹', '🚬'],
['💊', '🤖', 'ðŸŒ', '🕶ï¸'],
['💘', '🚬', 'ðŸ¸', '✈ï¸', '🇲🇦'],
['ðŸº', 'ðŸ', '🌄', 'ðŸ”', '🤠'],
['ðŸŽ', '🔥', '🥷', '🇬🇧', 'âš”ï¸'],
['💕', 'âš”ï¸', 'ðŸŽ', '👸', 'ðŸ€'],
['🚗', 'â°', '🔬', '💥'],
['ðŸœï¸', 'ðŸ›', '🚀', '👑', '💧']]
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
You might answer
| ["Finding Nemo", "Toy Story"]]
|
.
Now give me answers for these movies:
| ['💀', '🎃', '👻', 'â„ï¸', '🎄'],
['🔫', '🕴ï¸', '💼', '🇮🇹', '🚬'],
['💊', '🤖', 'ðŸŒ', '🕶ï¸'],
['💘', '🚬', 'ðŸ¸', '✈ï¸', '🇲🇦'],
['ðŸº', 'ðŸ', '🌄', 'ðŸ”', '🤠'],
['ðŸŽ', '🔥', '🥷', '🇬🇧', 'âš”ï¸'],
['💕', 'âš”ï¸', 'ðŸŽ', '👸', 'ðŸ€'],
['🚗', 'â°', '🔬', '💥'],
['ðŸœï¸', 'ðŸ›', '🚀', '👑', '💧']]
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What are the names of the movie titles?
Output
I'll help you identify the movie titles based on the emoji descriptions. Here's the JSON list of movie titles:
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12 | [
"The Lion King",
"Nightmare Before Christmas",
"The Godfather",
"The Matrix",
"Casablanca",
"Raiders of the Lost Ark",
"V for Vendetta",
"Beauty and the Beast",
"Back to the Future",
"Dune"
]
|
Let me break down the emoji-to-movie reasoning:
1. ðŸ¦ðŸ‘‘ðŸŒðŸ’”â¤ï¸ = The Lion King (lions, crown, world, heartbreak)
2. 💀🎃👻â„ï¸ðŸŽ„ = Nightmare Before Christmas (death, Halloween, ghosts, winter, Christmas)
3. 🔫🕴ï¸ðŸ’¼ðŸ‡®ðŸ‡¹ðŸš¬ = The Godfather (gun, suits, Italy, smoking)
4. 💊🤖ðŸŒðŸ•¶ï¸ = The Matrix (pills, robots, worldwide, sunglasses)
5. 💘🚬ðŸ¸âœˆï¸ðŸ‡²ðŸ‡¦ = Casablanca (love, smoking, cocktail, airplane, Morocco)
6. ðŸºðŸðŸŒ„ðŸ”🤠= Raiders of the Lost Ark (ark, snake, landscape, searching, adventurer)
7. ðŸŽðŸ”¥ðŸ¥·ðŸ‡¬ðŸ‡§âš”ï¸ = V for Vendetta (mask, fire, ninja, Britain, sword)
8. 💕⚔ï¸ðŸŽðŸ‘¸ðŸ€ = Beauty and the Beast (love, sword, horse, princess, rat)
9. 🚗â°ðŸ”¬ðŸ’¥ = Back to the Future (car, time, science, explosion)
10. ðŸœï¸ðŸ›ðŸš€ðŸ‘‘💧 = Dune (desert, worm, spaceship, royalty, water)
Extract Json
I extracted the following JSON from that output:
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12 | [
"The Lion King",
"Nightmare Before Christmas",
"The Godfather",
"The Matrix",
"Casablanca",
"Raiders of the Lost Ark",
"V for Vendetta",
"Beauty and the Beast",
"Back to the Future",
"Dune"
]
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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
you might might answer
| {"Finding Nemo": ["ðŸ ", "ðŸŸ", "ðŸ¡", "ðŸ¬", "ðŸ³"],
"Toy Story": ["🚀", "âš”ï¸,", "🤖", "👽", "🌌"]}
|
.
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:
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
you might might answer
| {"Finding Nemo": ["ðŸ ", "ðŸŸ", "ðŸ¡", "ðŸ¬", "ðŸ³"],
"Toy Story": ["🚀", "âš”ï¸,", "🤖", "👽", "🌌"]}
|
.
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:
Give ONLY a JSON output. Nothing else.
Output
{
"The Lion King": ["ðŸ¦", "👑", "ðŸŒ", "💔", "✨"],
"The Nightmare Before Christmas": ["💀", "🎄", "🎃", "👻", "â„ï¸"],
"The Godfather": ["ðŸ", "🕴ï¸", "💼", "💰", "🚬"],
"The Matrix": ["🤖", "âš¡", "🕶ï¸", "💊", "ðŸŒ"],
"Casablanca": ["â¤ï¸", "💔", "🚬", "🎩", "🌙"],
"Raiders of the Lost Ark": ["ðŸº", "ðŸ", "🤠", "ðŸ”", "💎"],
"V for Vendetta": ["🕊ï¸", "🔥", "ðŸŽ", "✊", "🗽"],
"The Princess Bride": ["💕", "âš”ï¸", "👸", "ðŸŽ", "ðŸ’"],
"Back to the Future": ["🚗", "â°", "🔬", "🕰ï¸", "👨"],
"Dune": ["ðŸœï¸", "ðŸ›", "👽", "💧", "👑"]
}
Extract Json
I extracted the following JSON from that output:
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12 | {
"The Lion King": ["ðŸ¦", "👑", "ðŸŒ", "💔", "✨"],
"The Nightmare Before Christmas": ["💀", "🎄", "🎃", "👻", "â„ï¸"],
"The Godfather": ["ðŸ", "🕴ï¸", "💼", "💰", "🚬"],
"The Matrix": ["🤖", "âš¡", "🕶ï¸", "💊", "ðŸŒ"],
"Casablanca": ["â¤ï¸", "💔", "🚬", "🎩", "🌙"],
"Raiders of the Lost Ark": ["ðŸº", "ðŸ", "🤠", "ðŸ”", "💎"],
"V for Vendetta": ["🕊ï¸", "🔥", "ðŸŽ", "✊", "🗽"],
"The Princess Bride": ["💕", "âš”ï¸", "👸", "ðŸŽ", "ðŸ’"],
"Back to the Future": ["🚗", "â°", "🔬", "🕰ï¸", "👨"],
"Dune": ["ðŸœï¸", "ðŸ›", "👽", "💧", "👑"]
}
|
PyFunc
Resulting in output:
[['ðŸ¦', '👑', 'ðŸŒ', '💔', '✨'],
['💀', '🎄', '🎃', '👻', 'â„ï¸'],
['ðŸ', '🕴ï¸', '💼', '💰', '🚬'],
['🤖', 'âš¡', '🕶ï¸', '💊', 'ðŸŒ'],
['â¤ï¸', '💔', '🚬', '🎩', '🌙'],
['ðŸº', 'ðŸ', '🤠', 'ðŸ”', '💎'],
['🕊ï¸', '🔥', 'ðŸŽ', '✊', '🗽'],
['💕', 'âš”ï¸', '👸', 'ðŸŽ', 'ðŸ’'],
['🚗', 'â°', '🔬', '🕰ï¸', '👨'],
['ðŸœï¸', 'ðŸ›', '👽', '💧', '👑']]
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
You might answer
| ["Finding Nemo", "Toy Story"]]
|
.
Now give me answers for these movies:
| ['💀', '🎄', '🎃', '👻', 'â„ï¸'],
['ðŸ', '🕴ï¸', '💼', '💰', '🚬'],
['🤖', 'âš¡', '🕶ï¸', '💊', 'ðŸŒ'],
['â¤ï¸', '💔', '🚬', '🎩', '🌙'],
['ðŸº', 'ðŸ', '🤠', 'ðŸ”', '💎'],
['🕊ï¸', '🔥', 'ðŸŽ', '✊', '🗽'],
['💕', 'âš”ï¸', '👸', 'ðŸŽ', 'ðŸ’'],
['🚗', 'â°', '🔬', '🕰ï¸', '👨'],
['ðŸœï¸', 'ðŸ›', '👽', '💧', '👑']]
|
What are the names of the movie titles?
Output
Here's the JSON list of movie titles corresponding to the emoji sequences:
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12 | [
"The Lion King",
"A Nightmare Before Christmas",
"Goodfellas",
"The Matrix",
"Casablanca",
"Indiana Jones and the Raiders of the Lost Ark",
"Hamilton",
"The Princess Bride",
"Interstellar",
"Dune"
]
|
These titles match the emoji sequences based on key themes, plot elements, or iconic imagery from each film.
Extract Json
I extracted the following JSON from that output:
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12 | [
"The Lion King",
"A Nightmare Before Christmas",
"Goodfellas",
"The Matrix",
"Casablanca",
"Indiana Jones and the Raiders of the Lost Ark",
"Hamilton",
"The Princess Bride",
"Interstellar",
"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
you might might answer
| {"Finding Nemo": ["ðŸ ", "ðŸŸ", "ðŸ¡", "ðŸ¬", "ðŸ³"],
"Toy Story": ["🚀", "âš”ï¸,", "🤖", "👽", "🌌"]}
|
.
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:
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
you might might answer
| {"Finding Nemo": ["ðŸ ", "ðŸŸ", "ðŸ¡", "ðŸ¬", "ðŸ³"],
"Toy Story": ["🚀", "âš”ï¸,", "🤖", "👽", "🌌"]}
|
.
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:
Give ONLY a JSON output. Nothing else.
Output
{
"The Lion King": ["👑", "ðŸ¦", "🌅", "ðŸ’", "💔"],
"The Nightmare Before Christmas": ["💀", "🎃", "🎶", "👻", "â„ï¸"],
"The Godfather": ["🕴ï¸", "💼", "🔫", "💰", "👨"],
"The Matrix": ["🤖", "🕶ï¸", "💊", "âš¡", "ðŸŒ"],
"Casablanca": ["💕", "🚬", "🎶", "🕊ï¸", "☕"],
"Raiders of the Lost Ark": ["ðŸº", "ðŸ", "â›ï¸", "ðŸŒ", "🤠"],
"V for Vendetta": ["ðŸŽ", "🔥", "✊", "🗽", "🕶ï¸"],
"The Princess Bride": ["💘", "âš”ï¸", "ðŸ°", "ðŸ’", "ðŸŽ"],
"Back to the Future": ["🚗", "â°", "🔬", "👨", "🌟"],
"Dune": ["ðŸœï¸", "ðŸ›", "👑", "🚀", "âš”ï¸"]
}
Extract Json
I extracted the following JSON from that output:
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12 | {
"The Lion King": ["👑", "ðŸ¦", "🌅", "ðŸ’", "💔"],
"The Nightmare Before Christmas": ["💀", "🎃", "🎶", "👻", "â„ï¸"],
"The Godfather": ["🕴ï¸", "💼", "🔫", "💰", "👨"],
"The Matrix": ["🤖", "🕶ï¸", "💊", "âš¡", "ðŸŒ"],
"Casablanca": ["💕", "🚬", "🎶", "🕊ï¸", "☕"],
"Raiders of the Lost Ark": ["ðŸº", "ðŸ", "â›ï¸", "ðŸŒ", "🤠"],
"V for Vendetta": ["ðŸŽ", "🔥", "✊", "🗽", "🕶ï¸"],
"The Princess Bride": ["💘", "âš”ï¸", "ðŸ°", "ðŸ’", "ðŸŽ"],
"Back to the Future": ["🚗", "â°", "🔬", "👨", "🌟"],
"Dune": ["ðŸœï¸", "ðŸ›", "👑", "🚀", "âš”ï¸"]
}
|
PyFunc
Resulting in output:
[['👑', 'ðŸ¦', '🌅', 'ðŸ’', '💔'],
['💀', '🎃', '🎶', '👻', 'â„ï¸'],
['🕴ï¸', '💼', '🔫', '💰', '👨'],
['🤖', '🕶ï¸', '💊', 'âš¡', 'ðŸŒ'],
['💕', '🚬', '🎶', '🕊ï¸', '☕'],
['ðŸº', 'ðŸ', 'â›ï¸', 'ðŸŒ', '🤠'],
['ðŸŽ', '🔥', '✊', '🗽', '🕶ï¸'],
['💘', 'âš”ï¸', 'ðŸ°', 'ðŸ’', 'ðŸŽ'],
['🚗', 'â°', '🔬', '👨', '🌟'],
['ðŸœï¸', 'ðŸ›', '👑', '🚀', 'âš”ï¸']]
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
You might answer
| ["Finding Nemo", "Toy Story"]]
|
.
Now give me answers for these movies:
| ['💀', '🎃', '🎶', '👻', 'â„ï¸'],
['🕴ï¸', '💼', '🔫', '💰', '👨'],
['🤖', '🕶ï¸', '💊', 'âš¡', 'ðŸŒ'],
['💕', '🚬', '🎶', '🕊ï¸', '☕'],
['ðŸº', 'ðŸ', 'â›ï¸', 'ðŸŒ', '🤠'],
['ðŸŽ', '🔥', '✊', '🗽', '🕶ï¸'],
['💘', 'âš”ï¸', 'ðŸ°', 'ðŸ’', 'ðŸŽ'],
['🚗', 'â°', '🔬', '👨', '🌟'],
['ðŸœï¸', 'ðŸ›', '👑', '🚀', 'âš”ï¸']]
|
What are the names of the movie titles?
Output
Here's the JSON list of movie titles corresponding to the emoji descriptions:
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12 | [
"The Lion King",
"The Nightmare Before Christmas",
"Pulp Fiction",
"The Matrix",
"La La Land",
"Indiana Jones and the Raiders of the Lost Ark",
"V for Vendetta",
"The Lord of the Rings",
"Interstellar",
"Dune"
]
|
Let me briefly explain some of the emoji mappings:
1. 👑ðŸ¦ðŸŒ…ðŸ’💔 = The Lion King (crown, lion, sunset, monkey, heartbreak)
2. 💀🎃🎶👻â„ï¸ = The Nightmare Before Christmas (skull, pumpkin, music, ghost, snow)
3. 🕴ï¸ðŸ’¼ðŸ”«ðŸ’°ðŸ‘¨ = Pulp Fiction (suit, briefcase, gun, money, man)
4. 🤖🕶ï¸ðŸ’Šâš¡ðŸŒ = The Matrix (robot, sunglasses, pill, lightning, world)
5. 💕🚬🎶🕊ï¸â˜• = La La Land (love, cigarette, music, dove, coffee)
... and so on.
Extract Json
I extracted the following JSON from that output:
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12 | [
"The Lion King",
"The Nightmare Before Christmas",
"Pulp Fiction",
"The Matrix",
"La La Land",
"Indiana Jones and the Raiders of the Lost Ark",
"V for Vendetta",
"The Lord of the Rings",
"Interstellar",
"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
you might might answer
| {"Finding Nemo": ["ðŸ ", "ðŸŸ", "ðŸ¡", "ðŸ¬", "ðŸ³"],
"Toy Story": ["🚀", "âš”ï¸,", "🤖", "👽", "🌌"]}
|
.
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:
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
you might might answer
| {"Finding Nemo": ["ðŸ ", "ðŸŸ", "ðŸ¡", "ðŸ¬", "ðŸ³"],
"Toy Story": ["🚀", "âš”ï¸,", "🤖", "👽", "🌌"]}
|
.
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:
Give ONLY a JSON output. Nothing else.
Output
{
"The Lion King": ["ðŸ¦", "👑", "🌅", "💔", "ðŸ’"],
"The Nightmare Before Christmas": ["💀", "🎃", "🌙", "â„ï¸", "ðŸŽ"],
"The Godfather": ["🚬", "🔫", "👔", "💼", "🇮🇹"],
"The Matrix": ["💊", "🤖", "ðŸŒ", "👨â€ðŸ’»", "âš¡"],
"Casablanca": ["💕", "🚬", "🎩", "ðŸŒ", "ðŸ¸"],
"Raiders of the Lost Ark": ["ðŸº", "ðŸ", "ðŸ”", "🌄", "🤠"],
"V for Vendetta": ["🔥", "ðŸŽ", "💣", "🕺", "🇬🇧"],
"The Princess Bride": ["âš”ï¸", "💘", "ðŸŽ", "ðŸ°", "😂"],
"Back to the Future": ["🚗", "â°", "🔬", "👨", "🌠"],
"Dune": ["ðŸœï¸", "ðŸ›", "👑", "🚀", "âš”ï¸"]
}
Extract Json
I extracted the following JSON from that output:
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12 | {
"The Lion King": ["ðŸ¦", "👑", "🌅", "💔", "ðŸ’"],
"The Nightmare Before Christmas": ["💀", "🎃", "🌙", "â„ï¸", "ðŸŽ"],
"The Godfather": ["🚬", "🔫", "👔", "💼", "🇮🇹"],
"The Matrix": ["💊", "🤖", "ðŸŒ", "👨â€ðŸ’»", "âš¡"],
"Casablanca": ["💕", "🚬", "🎩", "ðŸŒ", "ðŸ¸"],
"Raiders of the Lost Ark": ["ðŸº", "ðŸ", "ðŸ”", "🌄", "🤠"],
"V for Vendetta": ["🔥", "ðŸŽ", "💣", "🕺", "🇬🇧"],
"The Princess Bride": ["âš”ï¸", "💘", "ðŸŽ", "ðŸ°", "😂"],
"Back to the Future": ["🚗", "â°", "🔬", "👨", "🌠"],
"Dune": ["ðŸœï¸", "ðŸ›", "👑", "🚀", "âš”ï¸"]
}
|
PyFunc
Resulting in output:
[['ðŸ¦', '👑', '🌅', '💔', 'ðŸ’'],
['💀', '🎃', '🌙', 'â„ï¸', 'ðŸŽ'],
['🚬', '🔫', '👔', '💼', '🇮🇹'],
['💊', '🤖', 'ðŸŒ', 'âš¡'],
['💕', '🚬', '🎩', 'ðŸŒ', 'ðŸ¸'],
['ðŸº', 'ðŸ', 'ðŸ”', '🌄', '🤠'],
['🔥', 'ðŸŽ', '💣', '🕺', '🇬🇧'],
['âš”ï¸', '💘', 'ðŸŽ', 'ðŸ°', '😂'],
['🚗', 'â°', '🔬', '👨', '🌠'],
['ðŸœï¸', 'ðŸ›', '👑', '🚀', 'âš”ï¸']]
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
You might answer
| ["Finding Nemo", "Toy Story"]]
|
.
Now give me answers for these movies:
| ['💀', '🎃', '🌙', 'â„ï¸', 'ðŸŽ'],
['🚬', '🔫', '👔', '💼', '🇮🇹'],
['💊', '🤖', 'ðŸŒ', 'âš¡'],
['💕', '🚬', '🎩', 'ðŸŒ', 'ðŸ¸'],
['ðŸº', 'ðŸ', 'ðŸ”', '🌄', '🤠'],
['🔥', 'ðŸŽ', '💣', '🕺', '🇬🇧'],
['âš”ï¸', '💘', 'ðŸŽ', 'ðŸ°', '😂'],
['🚗', 'â°', '🔬', '👨', '🌠'],
['ðŸœï¸', 'ðŸ›', '👑', '🚀', 'âš”ï¸']]
|
What are the names of the movie titles?
Output
I'll solve this for you. Here's the JSON list of movie titles:
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12 | [
"The Lion King",
"Nightmare Before Christmas",
"Goodfellas",
"Lucy",
"Casablanca",
"Raiders of the Lost Ark",
"V for Vendetta",
"A Knight's Tale",
"Interstellar",
"Dune"
]
|
Let me break down how I interpreted each emoji set:
1. ðŸ¦ðŸ‘‘🌅💔💠- "The Lion King" (lion, crown, sunset, heartbreak, monkey)
2. 💀🎃🌙â„ï¸ðŸŽ - "Nightmare Before Christmas" (skull, pumpkin, moon, snowflake, mask)
3. 🚬🔫👔💼🇮🇹 - "Goodfellas" (cigarette, gun, suit, briefcase, Italy flag)
4. 💊🤖ðŸŒâš¡ - "Lucy" (pill, robot/AI, global network, lightning)
5. 💕🚬🎩ðŸŒðŸ¸ - "Casablanca" (love, cigarette, hat, world, martini)
6. ðŸºðŸðŸ”🌄🤠- "Raiders of the Lost Ark" (ark, snake, searching, landscape, adventurer)
7. 🔥ðŸŽðŸ’£ðŸ•ºðŸ‡¬ðŸ‡§ - "V for Vendetta" (fire, mask, bomb, dancing, UK flag)
8. âš”ï¸ðŸ’˜ðŸŽðŸ°ðŸ˜‚ - "A Knight's Tale" (sword, love, horse, castle, laugh)
9. 🚗â°ðŸ”¬ðŸ‘¨ðŸŒ - "Interstellar" (car, clock, science, man, stars)
10. ðŸœï¸ðŸ›ðŸ‘‘ðŸš€âš”ï¸ - "Dune" (desert, worm, royalty, rocket/space, battle)
Extract Json
I extracted the following JSON from that output:
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12 | [
"The Lion King",
"Nightmare Before Christmas",
"Goodfellas",
"Lucy",
"Casablanca",
"Raiders of the Lost Ark",
"V for Vendetta",
"A Knight's Tale",
"Interstellar",
"Dune"
]
|
PyFunc
OK
Resulting in output:
False