You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 movie_3
【Schema】
# Table: country
[
(country_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(country:TEXT, Examples: [Afghanistan, Algeria, American Samoa]),
(last_update:DATETIME, Examples: [2006-02-15 04:44:00])
]
# Table: customer
[
(customer_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(store_id:INTEGER, Examples: [1, 2]),
(first_name:TEXT, Examples: [MARY, PATRICIA, LINDA]),
(last_name:TEXT, Examples: [SMITH, JOHNSON, WILLIAMS]),
(email:TEXT),
(address_id:INTEGER, Examples: [5, 6, 7]),
(active:INTEGER, Examples: [1, 0]),
(create_date:DATETIME, Examples: [2006-02-14 22:04:36]),
(last_update:DATETIME, Examples: [2006-02-15 04:57:20])
]
# Table: film_text
[
(film_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(title:TEXT, Examples: [ACADEMY DINOSAUR, ACE GOLDFINGER, ADAPTATION HOLES]),
(description:TEXT)
]
# Table: store
[
(store_id:INTEGER, Primary Key, Examples: [1, 2]),
(manager_staff_id:INTEGER, Examples: [1, 2]),
(address_id:INTEGER, Examples: [1, 2]),
(last_update:DATETIME, Examples: [2006-02-15 04:57:12])
]
# Table: film_category
[
(film_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(category_id:INTEGER, Primary Key, Examples: [6, 11, 8]),
(last_update:DATETIME, Examples: [2006-02-15 05:07:09])
]
# Table: payment
[
(payment_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(customer_id:INTEGER, Examples: [1, 2, 3]),
(staff_id:INTEGER, Examples: [1, 2]),
(rental_id:INTEGER, Examples: [76, 573, 1185]),
(amount:REAL, Examples: [2.99, 0.99, 5.99]),
(payment_date:DATETIME, Examples: [2005-05-25 11:30:37]),
(last_update:DATETIME, Examples: [2006-02-15 22:12:30])
]
# Table: actor
[
(actor_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(first_name:TEXT, Examples: [PENELOPE, NICK, ED]),
(last_name:TEXT, Examples: [GUINESS, WAHLBERG, CHASE]),
(last_update:DATETIME, Examples: [2006-02-15 04:34:33])
]
# Table: rental
[
(rental_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(rental_date:DATETIME, Examples: [2005-05-24 22:53:30]),
(inventory_id:INTEGER, Examples: [367, 1525, 1711]),
(customer_id:INTEGER, Examples: [130, 459, 408]),
(return_date:DATETIME, Examples: [2005-05-26 22:04:30]),
(staff_id:INTEGER, Examples: [1, 2]),
(last_update:DATETIME, Examples: [2006-02-15 21:30:53])
]
# Table: language
[
(language_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(name:TEXT, Examples: [English, Italian, Japanese]),
(last_update:DATETIME, Examples: [2006-02-15 05:02:19])
]
# Table: film
[
(film_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(title:TEXT, Examples: [ACADEMY DINOSAUR, ACE GOLDFINGER, ADAPTATION HOLES]),
(description:TEXT),
(release_year:TEXT, Examples: [2006]),
(language_id:INTEGER, Examples: [1]),
(original_language_id:INTEGER),
(rental_duration:INTEGER, Examples: [6, 3, 7]),
(rental_rate:REAL, Examples: [0.99, 4.99, 2.99]),
(length:INTEGER, Examples: [86, 48, 50]),
(replacement_cost:REAL, Examples: [20.99, 12.99, 18.99]),
(rating:TEXT, Examples: [PG, G, NC-17]),
(special_features:TEXT, Examples: [Deleted Scenes,Behind the Scenes]),
(last_update:DATETIME, Examples: [2006-02-15 05:03:42])
]
# Table: inventory
[
(inventory_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(film_id:INTEGER, Examples: [1, 2, 3]),
(store_id:INTEGER, Examples: [1, 2]),
(last_update:DATETIME, Examples: [2006-02-15 05:09:17])
]
# Table: address
[
(address_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(address:TEXT, Examples: [47 MySakila Drive, 28 MySQL Boulevard, 23 Workhaven Lane]),
(address2:TEXT),
(district:TEXT, Examples: [Alberta, QLD, Nagasaki]),
(city_id:INTEGER, Examples: [300, 576, 463]),
(postal_code:TEXT, Examples: [35200, 17886, 83579]),
(phone:TEXT, Examples: [14033335568, 6172235589, 28303384290]),
(last_update:DATETIME, Examples: [2006-02-15 04:45:30])
]
# Table: film_actor
[
(actor_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(film_id:INTEGER, Primary Key, Examples: [1, 23, 25]),
(last_update:DATETIME, Examples: [2006-02-15 05:05:03])
]
# Table: category
[
(category_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(name:TEXT, Examples: [Action, Animation, Children]),
(last_update:DATETIME, Examples: [2006-02-15 04:46:27])
]
# Table: city
[
(city_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(city:TEXT, Examples: [A Corua (La Corua), Abha, Abu Dhabi]),
(country_id:INTEGER, Examples: [87, 82, 101]),
(last_update:DATETIME, Examples: [2006-02-15 04:45:25])
]
# Table: staff
[
(staff_id:INTEGER, Primary Key, Examples: [1, 2]),
(first_name:TEXT, Examples: [Mike, Jon]),
(last_name:TEXT, Examples: [Hillyer, Stephens]),
(address_id:INTEGER, Examples: [3, 4]),
(picture:BLOB),
(email:TEXT),
(store_id:INTEGER, Examples: [1, 2]),
(active:INTEGER, Examples: [1]),
(username:TEXT, Examples: [Mike, Jon]),
(password:TEXT, Examples: [8cb2237d0679ca88db6464eac60da96345513964]),
(last_update:DATETIME, Examples: [2006-02-15 04:57:16])
]
【Foreign keys】
customer.address_id=address.address_id
customer.store_id=store.store_id
store.address_id=address.address_id
store.manager_staff_id=staff.staff_id
film_category.category_id=category.category_id
film_category.film_id=film.film_id
payment.rental_id=rental.rental_id
payment.staff_id=staff.staff_id
payment.customer_id=customer.customer_id
rental.staff_id=staff.staff_id
rental.customer_id=customer.customer_id
rental.inventory_id=inventory.inventory_id
film.original_language_id=language.language_id
film.language_id=language.language_id
inventory.store_id=store.store_id
inventory.film_id=film.film_id
address.city_id=city.city_id
film_actor.film_id=film.film_id
film_actor.actor_id=actor.actor_id
city.country_id=country.country_id
staff.store_id=store.store_id
staff.address_id=address.address_id

    【Question】
    Calculate the percentage of movie titles with a screen length of more than 120 minutes that have a category of horror movies. screen length of more than 120 minutes refers to length > 120; category of horror refers to category.name = 'Horror'; percentage = divide(count(title where length > 120 and category.name = 'Horror'), count(title)) * 100%

    【Evidence】
    screen length of more than 120 minutes refers to length > 120; category of horror refers to category.name = 'Horror'; percentage = divide(count(title where length > 120 and category.name = 'Horror'), count(title)) * 100%

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST(SUM(IIF(T3.`name` = 'Horror', 1, 0)) * 100 / COUNT(T1.film_id) AS REAL) FROM film AS T1 INNER JOIN film_category AS T2 ON T1.film_id = T2.film_id INNER JOIN category AS T3 ON T3.category_id = T2.category_id WHERE T1.length > 120