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": ["π ", "π", "π‘", "π¬", "π³"],
"Toy Story": ["π", "βοΈ,", "π€", "π½", "π"]}
|
.
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": ["π ", "π", "π‘", "π¬", "π³"],
"Toy Story": ["π", "βοΈ,", "π€", "π½", "π"]}
|
.
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": ["π¦", "π", "π", "πΏ", "π"],
"The Nightmare Before Christmas": ["π", "π»", "π€‘", "β οΈ", "βοΈ"],
"The Godfather": ["πββοΈ", "π·", "πΏ", "π°", "π΅οΈ"],
"The Matrix": ["π€", "π", "πΆοΈ", "π₯", "π"],
"Casablanca": ["π©οΈ", "π", "πΈ", "π©", "π°"],
"Raiders of the Lost Ark": ["π΅οΈββοΈ", "πΊοΈ", "πΊ", "π", "π"],
"V for Vendetta": ["π¦ΈββοΈ", "π£", "π₯", "π¦", "β"],
"The Princess Bride": ["πΈ", "π€΅", "π‘οΈ", "π", "πΈ"],
"Back to the Future": ["β°", "π", "β‘οΈ", "πΈ", "π¨βπ¬"],
"Dune": ["π", "π", "π", "π", "ποΈ"]
}
Extract Json
I extracted the following JSON from that output:
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12 | {
"The Lion King": ["π¦", "π", "π", "πΏ", "π"],
"The Nightmare Before Christmas": ["π", "π»", "π€‘", "β οΈ", "βοΈ"],
"The Godfather": ["πββοΈ", "π·", "πΏ", "π°", "π΅οΈ"],
"The Matrix": ["π€", "π", "πΆοΈ", "π₯", "π"],
"Casablanca": ["π©οΈ", "π", "πΈ", "π©", "π°"],
"Raiders of the Lost Ark": ["π΅οΈββοΈ", "πΊοΈ", "πΊ", "π", "π"],
"V for Vendetta": ["π¦ΈββοΈ", "π£", "π₯", "π¦", "β"],
"The Princess Bride": ["πΈ", "π€΅", "π‘οΈ", "π", "πΈ"],
"Back to the Future": ["β°", "π", "β‘οΈ", "πΈ", "π¨βπ¬"],
"Dune": ["π", "π", "π", "π", "ποΈ"]
}
|
PyFunc
Resulting in output:
[['π¦', 'π', 'π', 'πΏ', 'π'],
['π', 'π»', 'π€‘', 'β οΈ', 'βοΈ'],
['π·', 'πΏ', 'π°', 'π΅οΈ'],
['π€', 'π', 'πΆοΈ', 'π₯', 'π'],
['π©οΈ', 'π', 'πΈ', 'π©', 'π°'],
['πΊοΈ', 'πΊ', 'π', 'π'],
['π£', 'π₯', 'π¦', 'β'],
['πΈ', 'π€΅', 'π‘οΈ', 'π', 'πΈ'],
['β°', 'π', 'β‘οΈ', 'πΈ'],
['π', 'π', 'π', 'π', 'ποΈ']]
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:
| ['π', 'π»', 'π€‘', 'β οΈ', 'βοΈ'],
['π·', 'πΏ', 'π°', 'π΅οΈ'],
['π€', 'π', 'πΆοΈ', 'π₯', 'π'],
['π©οΈ', 'π', 'πΈ', 'π©', 'π°'],
['πΊοΈ', 'πΊ', 'π', 'π'],
['π£', 'π₯', 'π¦', 'β'],
['πΈ', 'π€΅', 'π‘οΈ', 'π', 'πΈ'],
['β°', 'π', 'β‘οΈ', 'πΈ'],
['π', 'π', 'π', 'π', 'ποΈ']]
|
What are the names of the movie titles?
Output
[
"The Lion King",
"The Nightmare Before Christmas",
"A Walk in the Woods",
"The Matrix",
"The Hangover",
"The Mummy",
"Die Hard",
"The Princess Bride",
"Back to the Future",
"Star Wars: The Last Jedi"
]
Extract Json
I extracted the following JSON from that output:
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12 | [
"The Lion King",
"The Nightmare Before Christmas",
"A Walk in the Woods",
"The Matrix",
"The Hangover",
"The Mummy",
"Die Hard",
"The Princess Bride",
"Back to the Future",
"Star Wars: The Last Jedi"
]
|
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": ["π ", "π", "π‘", "π¬", "π³"],
"Toy Story": ["π", "βοΈ,", "π€", "π½", "π"]}
|
.
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": ["π ", "π", "π‘", "π¬", "π³"],
"Toy Story": ["π", "βοΈ,", "π€", "π½", "π"]}
|
.
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": ["π¦", "π", "π", "π", "π"],
"The Nightmare Before Christmas": ["π", "π", "π", "π»", "π΅"],
"The Godfather": ["π΅οΈββοΈ", "π·", "πΌ", "π¨βπ©βπ¦βπ¦", "π"],
"The Matrix": ["π€", "πΆοΈ", "πͺ", "π₯", "π"],
"Casablanca": ["π©οΈ", "π", "π¬", "π", "π©"],
"Raiders of the Lost Ark": ["π§", "πΊ", "π«", "π", "π«"],
"V for Vendetta": ["π£", "π¦ΈββοΈ", "π", "π¬π§", "β"],
"The Princess Bride": ["πΈ", "π", "π", "βοΈ", "π"],
"Back to the Future": ["β°", "π", "β‘οΈ", "πΈ", "π΄"],
"Dune": ["π", "π", "πͺ", "π", "π«"]
}
Extract Json
I extracted the following JSON from that output:
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12 | {
"The Lion King": ["π¦", "π", "π", "π", "π"],
"The Nightmare Before Christmas": ["π", "π", "π", "π»", "π΅"],
"The Godfather": ["π΅οΈββοΈ", "π·", "πΌ", "π¨βπ©βπ¦βπ¦", "π"],
"The Matrix": ["π€", "πΆοΈ", "πͺ", "π₯", "π"],
"Casablanca": ["π©οΈ", "π", "π¬", "π", "π©"],
"Raiders of the Lost Ark": ["π§", "πΊ", "π«", "π", "π«"],
"V for Vendetta": ["π£", "π¦ΈββοΈ", "π", "π¬π§", "β"],
"The Princess Bride": ["πΈ", "π", "π", "βοΈ", "π"],
"Back to the Future": ["β°", "π", "β‘οΈ", "πΈ", "π΄"],
"Dune": ["π", "π", "πͺ", "π", "π«"]
}
|
PyFunc
Resulting in output:
[['π¦', 'π', 'π', 'π', 'π'],
['π', 'π', 'π', 'π»', 'π΅'],
['π·', 'πΌ', 'π'],
['π€', 'πΆοΈ', 'πͺ', 'π₯', 'π'],
['π©οΈ', 'π', 'π¬', 'π', 'π©'],
['π§', 'πΊ', 'π«', 'π', 'π«'],
['π£', 'π', 'π¬π§', 'β'],
['πΈ', 'π', 'π', 'βοΈ', 'π'],
['β°', 'π', 'β‘οΈ', 'πΈ', 'π΄'],
['π', 'π', 'πͺ', 'π', 'π«']]
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:
| ['π', 'π', 'π', 'π»', 'π΅'],
['π·', 'πΌ', 'π'],
['π€', 'πΆοΈ', 'πͺ', 'π₯', 'π'],
['π©οΈ', 'π', 'π¬', 'π', 'π©'],
['π§', 'πΊ', 'π«', 'π', 'π«'],
['π£', 'π', 'π¬π§', 'β'],
['πΈ', 'π', 'π', 'βοΈ', 'π'],
['β°', 'π', 'β‘οΈ', 'πΈ', 'π΄'],
['π', 'π', 'πͺ', 'π', 'π«']]
|
What are the names of the movie titles?
Output
[
"The Lion King",
"The Nightmare Before Christmas",
"The Secretariat",
"Terminator 2: Judgment Day",
"Catch Me If You Can",
"The Mummy",
"V for Vendetta",
"The Princess Bride",
"Back to the Future",
"The NeverEnding Story"
]
Explanation:
1. ['π¦', 'π', 'π', 'π', 'π'] - The Lion King (lion, crown, earth, sun, moon)
2. ['π', 'π', 'π', 'π»', 'π΅'] - The Nightmare Before Christmas (pumpkin, skull, Christmas tree, ghost, music)
3. ['π·', 'πΌ', 'π'] - The Secretariat (wine glass, briefcase, broken heart)
4. ['π€', 'πΆοΈ', 'πͺ', 'π₯', 'π'] - Terminator 2: Judgment Day (robot, sunglasses, tornado, explosion, rainbow)
5. ['π©οΈ', 'π', 'π¬', 'π', 'π©'] - Catch Me If You Can (airplane, ring, cigarette, kiss, hat)
6. ['π§', 'πΊ', 'π«', 'π', 'π«'] - The Mummy (compass, amphora, camel, skull, gun)
7. ['π£', 'π', 'π¬π§', 'β'] - V for Vendetta (bomb, broken heart, Union Jack, fist)
8. ['πΈ', 'π', 'π', 'βοΈ', 'π'] - The Princess Bride (princess, snake, ring, sword, love)
9. ['β°', 'π', 'β‘οΈ', 'πΈ', 'π΄'] - Back to the Future (clock, car, lightning, guitar, old man)
10. ['π', 'π', 'πͺ', 'π', 'π«'] - The NeverEnding Story (galaxy, dragon, tornado, crown, horse)
Note: Some of these answers might not be the exact movies you are looking for, but they are the closest matches based on the given emojis.
Extract Json
I extracted the following JSON from that output:
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12 | [
"The Lion King",
"The Nightmare Before Christmas",
"The Secretariat",
"Terminator 2: Judgment Day",
"Catch Me If You Can",
"The Mummy",
"V for Vendetta",
"The Princess Bride",
"Back to the Future",
"The NeverEnding Story"
]
|
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": ["π ", "π", "π‘", "π¬", "π³"],
"Toy Story": ["π", "βοΈ,", "π€", "π½", "π"]}
|
.
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": ["π ", "π", "π‘", "π¬", "π³"],
"Toy Story": ["π", "βοΈ,", "π€", "π½", "π"]}
|
.
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": ["π¦", "π", "π", "π", "π "],
"The Nightmare Before Christmas": ["π", "π", "π", "π", "π»"],
"The Godfather": ["π¨βπ¦³", "π·", "πΌ", "π°", "π"],
"The Matrix": ["π€", "πΆοΈ", "π", "π", "π₯"],
"Casablanca": ["π©", "π¬", "π", "π©", "πΆ"],
"Raiders of the Lost Ark": ["π§", "π΅οΈββοΈ", "πΊ", "π", "π"],
"V for Vendetta": ["π¦ΈββοΈ", "π£", "π₯", "π", "π¬π§"],
"The Princess Bride": ["πΈββοΈ", "π€΄ββοΈ", "π‘οΈ", "π", "πΈ"],
"Back to the Future": ["β°", "π", "β‘", "π΄", "πΈ"],
"Dune": ["π", "π«", "π", "πͺοΈ", "π"]
}
Extract Json
I extracted the following JSON from that output:
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12 | {
"The Lion King": ["π¦", "π", "π", "π", "π "],
"The Nightmare Before Christmas": ["π", "π", "π", "π", "π»"],
"The Godfather": ["π¨βπ¦³", "π·", "πΌ", "π°", "π"],
"The Matrix": ["π€", "πΆοΈ", "π", "π", "π₯"],
"Casablanca": ["π©", "π¬", "π", "π©", "πΆ"],
"Raiders of the Lost Ark": ["π§", "π΅οΈββοΈ", "πΊ", "π", "π"],
"V for Vendetta": ["π¦ΈββοΈ", "π£", "π₯", "π", "π¬π§"],
"The Princess Bride": ["πΈββοΈ", "π€΄ββοΈ", "π‘οΈ", "π", "πΈ"],
"Back to the Future": ["β°", "π", "β‘", "π΄", "πΈ"],
"Dune": ["π", "π«", "π", "πͺοΈ", "π"]
}
|
PyFunc
Resulting in output:
[['π¦', 'π', 'π', 'π', 'π '],
['π', 'π', 'π', 'π', 'π»'],
['π·', 'πΌ', 'π°', 'π'],
['π€', 'πΆοΈ', 'π', 'π', 'π₯'],
['π©', 'π¬', 'π', 'π©', 'πΆ'],
['π§', 'πΊ', 'π', 'π'],
['π£', 'π₯', 'π', 'π¬π§'],
['π‘οΈ', 'π', 'πΈ'],
['β°', 'π', 'β‘', 'π΄', 'πΈ'],
['π', 'π«', 'π', 'πͺοΈ', 'π']]
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:
| ['π', 'π', 'π', 'π', 'π»'],
['π·', 'πΌ', 'π°', 'π'],
['π€', 'πΆοΈ', 'π', 'π', 'π₯'],
['π©', 'π¬', 'π', 'π©', 'πΆ'],
['π§', 'πΊ', 'π', 'π'],
['π£', 'π₯', 'π', 'π¬π§'],
['π‘οΈ', 'π', 'πΈ'],
['β°', 'π', 'β‘', 'π΄', 'πΈ'],
['π', 'π«', 'π', 'πͺοΈ', 'π']]
|
What are the names of the movie titles?
Output
Here are my answers:
| ["The Lion King",
"The Nightmare Before Christmas",
"The Wolf of Wall Street",
"Interstellar",
"Casablanca",
"Raiders of the Lost Ark",
"Blown Away",
"The Princess and the Frog",
"Back to the Future",
"Star Wars: The Last Jedi"]
|
Note: The movie titles are based on my interpretation of the emojis and may not be the only possible answers. For example, the third emoji sequence could also be interpreted as "The Big Short" or "Margin Call." Similarly, the fourth emoji sequence could also be interpreted as "Gravity" or "The Martian." The eighth emoji sequence could also be interpreted as "Christine" or "Drive." The last emoji sequence could also be interpreted as "The Lion, the Witch, and the Wardrobe" or "Dune."
Extract Json
I extracted the following JSON from that output:
| ["The Lion King",
"The Nightmare Before Christmas",
"The Wolf of Wall Street",
"Interstellar",
"Casablanca",
"Raiders of the Lost Ark",
"Blown Away",
"The Princess and the Frog",
"Back to the Future",
"Star Wars: The Last Jedi"]
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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": ["π ", "π", "π‘", "π¬", "π³"],
"Toy Story": ["π", "βοΈ,", "π€", "π½", "π"]}
|
.
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": ["π ", "π", "π‘", "π¬", "π³"],
"Toy Story": ["π", "βοΈ,", "π€", "π½", "π"]}
|
.
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": ["π¦", "π", "π", "π³", "π΅"],
"The Nightmare Before Christmas": ["π", "π", "π", "π»", "π΅"],
"The Godfather": ["π¨βπ¦²", "πΌ", "π·", "π°", "π΅οΈ"],
"The Matrix": ["π€", "πΆοΈ", "π΄π’π΅", "π₯", "π"],
"Casablanca": ["πββοΈ", "π", "π©", "πΈ", "π"],
"Raiders of the Lost Ark": ["π΅οΈββοΈ", "πΊ", "π", "πΊ", "π"],
"V for Vendetta": ["π¦ΈββοΈ", "π£", "π₯", "π", "πββοΈ"],
"The Princess Bride": ["πΈ", "π", "βοΈ", "π", "π"],
"Back to the Future": ["β", "π", "β‘οΈ", "πΈ", "π¨βπ¬"],
"Dune": ["π", "π«", "π", "πͺ", "π§"]
}
Extract Json
I extracted the following JSON from that output:
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12 | {
"The Lion King": ["π¦", "π", "π", "π³", "π΅"],
"The Nightmare Before Christmas": ["π", "π", "π", "π»", "π΅"],
"The Godfather": ["π¨βπ¦²", "πΌ", "π·", "π°", "π΅οΈ"],
"The Matrix": ["π€", "πΆοΈ", "π΄π’π΅", "π₯", "π"],
"Casablanca": ["πββοΈ", "π", "π©", "πΈ", "π"],
"Raiders of the Lost Ark": ["π΅οΈββοΈ", "πΊ", "π", "πΊ", "π"],
"V for Vendetta": ["π¦ΈββοΈ", "π£", "π₯", "π", "πββοΈ"],
"The Princess Bride": ["πΈ", "π", "βοΈ", "π", "π"],
"Back to the Future": ["β", "π", "β‘οΈ", "πΈ", "π¨βπ¬"],
"Dune": ["π", "π«", "π", "πͺ", "π§"]
}
|
PyFunc
Resulting in output:
[['π¦', 'π', 'π', 'π³', 'π΅'],
['π', 'π', 'π', 'π»', 'π΅'],
['πΌ', 'π·', 'π°', 'π΅οΈ'],
['π€', 'πΆοΈ', 'π₯', 'π'],
['π', 'π©', 'πΈ', 'π'],
['πΊ', 'π', 'πΊ', 'π'],
['π£', 'π₯', 'π'],
['πΈ', 'π', 'βοΈ', 'π', 'π'],
['β', 'π', 'β‘οΈ', 'πΈ'],
['π', 'π«', 'π', 'πͺ', 'π§']]
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:
| ['π', 'π', 'π', 'π»', 'π΅'],
['πΌ', 'π·', 'π°', 'π΅οΈ'],
['π€', 'πΆοΈ', 'π₯', 'π'],
['π', 'π©', 'πΈ', 'π'],
['πΊ', 'π', 'πΊ', 'π'],
['π£', 'π₯', 'π'],
['πΈ', 'π', 'βοΈ', 'π', 'π'],
['β', 'π', 'β‘οΈ', 'πΈ'],
['π', 'π«', 'π', 'πͺ', 'π§']]
|
What are the names of the movie titles?
Output
[
"The Lion King",
"The Nightmare Before Christmas",
"The Secretariat",
"WALL-E",
"Catch Me If You Can",
"The Mummy",
"Die Hard",
"The Princess and the Crocodile",
"Back to the Future",
"The Lion, the Witch, and the Wardrobe"
]
Note:
1. For the third movie, I assumed that the briefcase (πΌ) and wine glass (π·) emojis are meant to represent a business or corporate theme, and the detective (π΅οΈ) emoji implies some kind of investigation or thriller. "The Secretariat" is not a perfect match, but it is a movie about a successful racehorse business and features elements of investigation and intrigue.
2. For the ninth movie, I assumed that the hourglass (β) and lightning bolt (β‘οΈ) emojis are meant to represent time travel, and the car (π) and guitar (πΈ) emojis are meant to represent the 1950s or 1980s setting and the rock music theme. "Back to the Future" is the best match for these emojis.
However, please note that there may be other possible answers depending on the interpretation of the emojis.
Extract Json
I extracted the following JSON from that output:
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12 | [
"The Lion King",
"The Nightmare Before Christmas",
"The Secretariat",
"WALL-E",
"Catch Me If You Can",
"The Mummy",
"Die Hard",
"The Princess and the Crocodile",
"Back to the Future",
"The Lion, the Witch, and the Wardrobe"
]
|
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": ["π ", "π", "π‘", "π¬", "π³"],
"Toy Story": ["π", "βοΈ,", "π€", "π½", "π"]}
|
.
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": ["π ", "π", "π‘", "π¬", "π³"],
"Toy Story": ["π", "βοΈ,", "π€", "π½", "π"]}
|
.
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": ["π¦", "π", "π", "π", "π"],
"The Nightmare Before Christmas": ["π", "π", "π»", "π", "π "],
"The Godfather": ["πββοΈ", "π·", "πΏ", "π°", "π΅οΈββοΈ"],
"The Matrix": ["π₯οΈ", "πΆοΈ", "π€", "π΄", "π΅"],
"Casablanca": ["π©οΈ", "πΈ", "π", "β€οΈ", "π©"],
"Raiders of the Lost Ark": ["π΅οΈββοΈ", "πΊοΈ", "πΊ", "π", "π"],
"V for Vendetta": ["π€‘", "π£", "π₯", "β", "π¬π§"],
"The Princess Bride": ["πΈοΈ", "π€΄ββοΈ", "π‘οΈ", "π", "πΈ"],
"Back to the Future": ["βοΈ", "π", "β‘οΈ", "π΄ββοΈ", "π"],
"Dune": ["π", "πͺοΈ", "π", "π", "β οΈ"]
}
Extract Json
I extracted the following JSON from that output:
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12 | {
"The Lion King": ["π¦", "π", "π", "π", "π"],
"The Nightmare Before Christmas": ["π", "π", "π»", "π", "π "],
"The Godfather": ["πββοΈ", "π·", "πΏ", "π°", "π΅οΈββοΈ"],
"The Matrix": ["π₯οΈ", "πΆοΈ", "π€", "π΄", "π΅"],
"Casablanca": ["π©οΈ", "πΈ", "π", "β€οΈ", "π©"],
"Raiders of the Lost Ark": ["π΅οΈββοΈ", "πΊοΈ", "πΊ", "π", "π"],
"V for Vendetta": ["π€‘", "π£", "π₯", "β", "π¬π§"],
"The Princess Bride": ["πΈοΈ", "π€΄ββοΈ", "π‘οΈ", "π", "πΈ"],
"Back to the Future": ["βοΈ", "π", "β‘οΈ", "π΄ββοΈ", "π"],
"Dune": ["π", "πͺοΈ", "π", "π", "β οΈ"]
}
|
PyFunc
Resulting in output:
[['π¦', 'π', 'π', 'π', 'π'],
['π', 'π', 'π»', 'π', 'π '],
['π·', 'πΏ', 'π°'],
['π₯οΈ', 'πΆοΈ', 'π€', 'π΄', 'π΅'],
['π©οΈ', 'πΈ', 'π', 'β€οΈ', 'π©'],
['πΊοΈ', 'πΊ', 'π', 'π'],
['π€‘', 'π£', 'π₯', 'β', 'π¬π§'],
['πΈοΈ', 'π‘οΈ', 'π', 'πΈ'],
['βοΈ', 'π', 'β‘οΈ', 'π'],
['π', 'πͺοΈ', 'π', 'π', 'β οΈ']]
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:
| ['π', 'π', 'π»', 'π', 'π '],
['π·', 'πΏ', 'π°'],
['π₯οΈ', 'πΆοΈ', 'π€', 'π΄', 'π΅'],
['π©οΈ', 'πΈ', 'π', 'β€οΈ', 'π©'],
['πΊοΈ', 'πΊ', 'π', 'π'],
['π€‘', 'π£', 'π₯', 'β', 'π¬π§'],
['πΈοΈ', 'π‘οΈ', 'π', 'πΈ'],
['βοΈ', 'π', 'β‘οΈ', 'π'],
['π', 'πͺοΈ', 'π', 'π', 'β οΈ']]
|
What are the names of the movie titles?
Output
Here are my answers:
| ["The Lion King",
"The Nightmare Before Christmas",
"Sideways",
"Tron",
"Catch Me If You Can",
"Raiders of the Lost Ark",
"V for Vendetta",
"The Princess and the Frog",
"Back to the Future",
"Interstellar"]
|
Explanation:
1. The Lion King: The lion, crown, globe, sun, and moon represent the animals, royalty, setting, time of day, and natural phenomena in the film.
2. The Nightmare Before Christmas: The jack-o'-lantern, skull, ghost, Christmas tree, and shooting star represent the holidays, death, supernatural, and fantastical elements of the film.
3. Sideways: The wine glass, grapevine, and money represent the wine-tasting, vineyards, and financial struggles in the film.
4. Tron: The computer, sunglasses, robot, red, and blue represent the technology, virtual reality, artificial intelligence, and light cycles in the film.
5. Catch Me If You Can: The airplane, cocktail, kiss, heart, and hat represent the travel, romance, deception, love, and disguise in the film.
6. Raiders of the Lost Ark: The map, column, snake, and skull represent the exploration, ancient ruins, danger, and death in the film.
7. V for Vendetta: The clown, bomb, fire, fist, and British flag represent the anarchy, destruction, rebellion, and political themes in the film.
8. The Princess and the Frog: The princess, sword, love, and frog represent the royalty, conflict, romance, and transformation in the film.
9. Back to the Future: The watch, car, lightning bolt, and VHS tape represent the time travel, transportation, power, and nostalgia in the film.
10. Interstellar: The galaxy, tornado, caterpillar, crown, and skull represent the space exploration, natural disaster, evolution, leadership, and mortality in the film.
Extract Json
I extracted the following JSON from that output:
| ["The Lion King",
"The Nightmare Before Christmas",
"Sideways",
"Tron",
"Catch Me If You Can",
"Raiders of the Lost Ark",
"V for Vendetta",
"The Princess and the Frog",
"Back to the Future",
"Interstellar"]
|
PyFunc
OK
Resulting in output:
False