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": ["πŸš€", "βš”οΈ,", "πŸ€–", "πŸ‘½", "🌌"]}
. 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: 
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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": ["πŸš€", "βš”οΈ,", "πŸ€–", "πŸ‘½", "🌌"]}
. 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": ["🦁", "πŸŒ…", "πŸ‘‘", "🐾", "🌍"],
  "The Nightmare Before Christmas": ["πŸŽƒ", "πŸŽ„", "πŸŒ™", "πŸ‘»", "πŸ•ΈοΈ"],
  "The Godfather": ["πŸ‘”", "🍝", "πŸ•ΆοΈ", "πŸ”«", "🎩"],
  "The Matrix": ["πŸ•ΆοΈ", "πŸ’Š", "πŸ”«", "πŸ–₯️", "πŸ•³οΈ"],
  "Casablanca": ["✈️", "❀️", "πŸŒ†", "🎷", "β˜•"],
  "Raiders of the Lost Ark": ["🏺", "🧭", "πŸ—ΊοΈ", "βš”οΈ", "πŸ—‘οΈ"],
  "V for Vendetta": ["🎭", "πŸ’£", "🌹", "πŸ‡¬πŸ‡§", "πŸ—‘οΈ"],
  "The Princess Bride": ["βš”οΈ", "πŸ‘Έ", "πŸ’–", "🐴", "🌹"],
  "Back to the Future": ["πŸš—", "⏰", "⚑", "πŸ•°οΈ", "πŸ•ΆοΈ"],
  "Dune": ["🏜️", "πŸ›", "✨", "πŸ‘‘", "🌌"]
}

Extract Json

I extracted the following JSON from that output: 

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{
  "The Lion King": ["🦁", "πŸŒ…", "πŸ‘‘", "🐾", "🌍"],
  "The Nightmare Before Christmas": ["πŸŽƒ", "πŸŽ„", "πŸŒ™", "πŸ‘»", "πŸ•ΈοΈ"],
  "The Godfather": ["πŸ‘”", "🍝", "πŸ•ΆοΈ", "πŸ”«", "🎩"],
  "The Matrix": ["πŸ•ΆοΈ", "πŸ’Š", "πŸ”«", "πŸ–₯️", "πŸ•³οΈ"],
  "Casablanca": ["✈️", "❀️", "πŸŒ†", "🎷", "β˜•"],
  "Raiders of the Lost Ark": ["🏺", "🧭", "πŸ—ΊοΈ", "βš”οΈ", "πŸ—‘οΈ"],
  "V for Vendetta": ["🎭", "πŸ’£", "🌹", "πŸ‡¬πŸ‡§", "πŸ—‘οΈ"],
  "The Princess Bride": ["βš”οΈ", "πŸ‘Έ", "πŸ’–", "🐴", "🌹"],
  "Back to the Future": ["πŸš—", "⏰", "⚑", "πŸ•°οΈ", "πŸ•ΆοΈ"],
  "Dune": ["🏜️", "πŸ›", "✨", "πŸ‘‘", "🌌"]
}

PyFunc

Resulting in output: [['🦁', 'πŸŒ…', 'πŸ‘‘', '🐾', '🌍'], ['πŸŽƒ', 'πŸŽ„', 'πŸŒ™', 'πŸ‘»', 'πŸ•ΈοΈ'], ['πŸ‘”', '🍝', 'πŸ•ΆοΈ', 'πŸ”«', '🎩'], ['πŸ•ΆοΈ', 'πŸ’Š', 'πŸ”«', 'πŸ–₯️', 'πŸ•³οΈ'], ['✈️', '❀️', 'πŸŒ†', '🎷', 'β˜•'], ['🏺', '🧭', 'πŸ—ΊοΈ', 'βš”οΈ', 'πŸ—‘οΈ'], ['🎭', 'πŸ’£', '🌹', 'πŸ‡¬πŸ‡§', 'πŸ—‘οΈ'], ['βš”οΈ', 'πŸ‘Έ', 'πŸ’–', '🐴', '🌹'], ['πŸš—', '⏰', '⚑', 'πŸ•°οΈ', 'πŸ•ΆοΈ'], ['🏜️', 'πŸ›', '✨', 'πŸ‘‘', '🌌']]

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 
1
["Finding Nemo", "Toy Story"]]
. Now give me answers for these movies: 
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['πŸŽƒ', 'πŸŽ„', 'πŸŒ™', 'πŸ‘»', 'πŸ•ΈοΈ'],
['πŸ‘”', '🍝', 'πŸ•ΆοΈ', 'πŸ”«', '🎩'],
['πŸ•ΆοΈ', 'πŸ’Š', 'πŸ”«', 'πŸ–₯️', 'πŸ•³οΈ'],
['✈️', '❀️', 'πŸŒ†', '🎷', 'β˜•'],
['🏺', '🧭', 'πŸ—ΊοΈ', 'βš”οΈ', 'πŸ—‘οΈ'],
['🎭', 'πŸ’£', '🌹', 'πŸ‡¬πŸ‡§', 'πŸ—‘οΈ'],
['βš”οΈ', 'πŸ‘Έ', 'πŸ’–', '🐴', '🌹'],
['πŸš—', '⏰', '⚑', 'πŸ•°οΈ', 'πŸ•ΆοΈ'],
['🏜️', 'πŸ›', '✨', 'πŸ‘‘', '🌌']]
What are the names of the movie titles?

Output

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[
  "The Lion King",
  "The Nightmare Before Christmas",
  "The Godfather",
  "The Matrix",
  "Sleepless in Seattle",
  "Indiana Jones",
  "Shakespeare in Love",
  "Beauty and the Beast",
  "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",
  "Sleepless in Seattle",
  "Indiana Jones",
  "Shakespeare in Love",
  "Beauty and the Beast",
  "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": ["πŸš€", "βš”οΈ,", "πŸ€–", "πŸ‘½", "🌌"]}
. 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": ["πŸš€", "βš”οΈ,", "πŸ€–", "πŸ‘½", "🌌"]}
. 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": ["🦁", "πŸ‘‘", "πŸŒ…", "🐘", "πŸ¦“"],
  "The Nightmare Before Christmas": ["πŸŽƒ", "πŸŽ„", "πŸ•·", "πŸ‘»", "πŸ¦‡"],
  "The Godfather": ["🍝", "πŸ”«", "πŸ’Ό", "πŸ•Ά", "πŸ‘”"],
  "The Matrix": ["πŸ•Ά", "πŸ’Š", "🌐", "πŸ€–", "πŸ•³"],
  "Casablanca": ["πŸŽ₯", "🌴", "✈", "πŸ’”", "πŸ•Š"],
  "Raiders of the Lost Ark": ["πŸ—Ί", "🏺", "🏹", "πŸ›‘", "🏜"],
  "V for Vendetta": ["🎭", "πŸ•Ά", "πŸ—‘", "πŸ”₯", "πŸ‡¬πŸ‡§"],
  "The Princess Bride": ["βš”", "πŸ‘Έ", "πŸ‰", "🏰", "🌹"],
  "Back to the Future": ["πŸš—", "⏳", "⚑", "🚲", "πŸ•°"],
  "Dune": ["🏜", "πŸ›", "πŸ‘½", "πŸ‘‘", "πŸŒ€"]
}

Extract Json

I extracted the following JSON from that output: 

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{
  "The Lion King": ["🦁", "πŸ‘‘", "πŸŒ…", "🐘", "πŸ¦“"],
  "The Nightmare Before Christmas": ["πŸŽƒ", "πŸŽ„", "πŸ•·", "πŸ‘»", "πŸ¦‡"],
  "The Godfather": ["🍝", "πŸ”«", "πŸ’Ό", "πŸ•Ά", "πŸ‘”"],
  "The Matrix": ["πŸ•Ά", "πŸ’Š", "🌐", "πŸ€–", "πŸ•³"],
  "Casablanca": ["πŸŽ₯", "🌴", "✈", "πŸ’”", "πŸ•Š"],
  "Raiders of the Lost Ark": ["πŸ—Ί", "🏺", "🏹", "πŸ›‘", "🏜"],
  "V for Vendetta": ["🎭", "πŸ•Ά", "πŸ—‘", "πŸ”₯", "πŸ‡¬πŸ‡§"],
  "The Princess Bride": ["βš”", "πŸ‘Έ", "πŸ‰", "🏰", "🌹"],
  "Back to the Future": ["πŸš—", "⏳", "⚑", "🚲", "πŸ•°"],
  "Dune": ["🏜", "πŸ›", "πŸ‘½", "πŸ‘‘", "πŸŒ€"]
}

PyFunc

Resulting in output: [['🦁', 'πŸ‘‘', 'πŸŒ…', '🐘', 'πŸ¦“'], ['πŸŽƒ', 'πŸŽ„', 'πŸ•·', 'πŸ‘»', 'πŸ¦‡'], ['🍝', 'πŸ”«', 'πŸ’Ό', 'πŸ•Ά', 'πŸ‘”'], ['πŸ•Ά', 'πŸ’Š', '🌐', 'πŸ€–', 'πŸ•³'], ['πŸŽ₯', '🌴', '✈', 'πŸ’”', 'πŸ•Š'], ['πŸ—Ί', '🏺', '🏹', 'πŸ›‘', '🏜'], ['🎭', 'πŸ•Ά', 'πŸ—‘', 'πŸ”₯', 'πŸ‡¬πŸ‡§'], ['βš”', 'πŸ‘Έ', 'πŸ‰', '🏰', '🌹'], ['πŸš—', '⏳', '⚑', '🚲', 'πŸ•°'], ['🏜', 'πŸ›', 'πŸ‘½', 'πŸ‘‘', 'πŸŒ€']]

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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['πŸŽƒ', 'πŸŽ„', 'πŸ•·', 'πŸ‘»', 'πŸ¦‡'],
['🍝', 'πŸ”«', 'πŸ’Ό', 'πŸ•Ά', 'πŸ‘”'],
['πŸ•Ά', 'πŸ’Š', '🌐', 'πŸ€–', 'πŸ•³'],
['πŸŽ₯', '🌴', '✈', 'πŸ’”', 'πŸ•Š'],
['πŸ—Ί', '🏺', '🏹', 'πŸ›‘', '🏜'],
['🎭', 'πŸ•Ά', 'πŸ—‘', 'πŸ”₯', 'πŸ‡¬πŸ‡§'],
['βš”', 'πŸ‘Έ', 'πŸ‰', '🏰', '🌹'],
['πŸš—', '⏳', '⚑', '🚲', 'πŸ•°'],
['🏜', 'πŸ›', 'πŸ‘½', 'πŸ‘‘', 'πŸŒ€']]
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: Raiders of the Lost Ark",
  "James Bond",
  "Frozen",
  "Back to the Future",
  "Star Wars"
]

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: Raiders of the Lost Ark",
  "James Bond",
  "Frozen",
  "Back to the Future",
  "Star Wars"
]

PyFunc

OK Resulting in output: False