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

    【Schema】
    【DB_ID】 movies_4
【Schema】
# Table: language_role
[
(role_id:INTEGER, Primary Key, Examples: [1, 2]),
(language_role:TEXT, Examples: [Original, Spoken])
]
# Table: country
[
(country_id:INTEGER, Primary Key, Examples: [128, 129, 130]),
(country_iso_code:TEXT, Examples: [AE, AF, AO]),
(country_name:TEXT, Examples: [United Arab Emirates, Afghanistan, Angola])
]
# Table: production_country
[
(movie_id:INTEGER, Examples: [5, 11, 12]),
(country_id:INTEGER, Examples: [214, 131, 152])
]
# Table: department
[
(department_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(department_name:TEXT, Examples: [Camera, Directing, Production])
]
# Table: movie_keywords
[
(movie_id:INTEGER, Examples: [5, 11, 12]),
(keyword_id:INTEGER, Examples: [612, 613, 616])
]
# Table: movie_company
[
(movie_id:INTEGER, Examples: [5, 11, 12]),
(company_id:INTEGER, Examples: [14, 59, 1])
]
# Table: language
[
(language_id:INTEGER, Primary Key, Examples: [24574, 24575, 24576]),
(language_code:TEXT, Examples: [en, sv, de]),
(language_name:TEXT, Examples: [English, svenska, Deutsch])
]
# Table: movie_cast
[
(movie_id:INTEGER, Examples: [285, 559, 767]),
(person_id:INTEGER, Examples: [85, 114, 116]),
(character_name:TEXT, Examples: [Captain Jack Sparrow, Will Turner, Elizabeth Swann]),
(gender_id:INTEGER, Examples: [2, 1, 0]),
(cast_order:INTEGER, Examples: [0, 1, 2])
]
# Table: movie_crew
[
(movie_id:INTEGER, Examples: [285, 559, 767]),
(person_id:INTEGER, Examples: [120, 1704, 770]),
(department_id:INTEGER, Examples: [1, 2, 3]),
(job:TEXT, Examples: [Director of Photography])
]
# Table: person
[
(person_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(person_name:TEXT, Examples: [George Lucas, Mark Hamill, Harrison Ford])
]
# Table: keyword
[
(keyword_id:INTEGER, Primary Key, Examples: [30, 65, 74]),
(keyword_name:TEXT, Examples: [individual, holiday, germany])
]
# Table: production_company
[
(company_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(company_name:TEXT, Examples: [Lucasfilm])
]
# Table: genre
[
(genre_id:INTEGER, Primary Key, Examples: [12, 14, 16]),
(genre_name:TEXT, Examples: [Adventure, Fantasy, Animation])
]
# Table: gender
[
(gender_id:INTEGER, Primary Key, Examples: [0, 1, 2]),
(gender:TEXT, Examples: [Unspecified, Female, Male])
]
# Table: movie_languages
[
(movie_id:INTEGER, Examples: [5, 11, 12]),
(language_id:INTEGER, Examples: [24574, 24575, 24576]),
(language_role_id:INTEGER, Examples: [2, 1])
]
# Table: movie
[
(movie_id:INTEGER, Primary Key, Examples: [5, 11, 12]),
(title:TEXT, Examples: [Four Rooms, Star Wars, Finding Nemo]),
(budget:INTEGER, Examples: [4000000, 11000000, 94000000]),
(homepage:TEXT),
(overview:TEXT),
(popularity:REAL, Examples: [22.87623, 126.393695, 85.688789]),
(release_date:DATE, Examples: [1995-12-09]),
(revenue:INTEGER, Examples: [4300000, 775398007, 940335536]),
(runtime:INTEGER, Examples: [98, 121, 100]),
(movie_status:TEXT, Examples: [Released, Rumored, Post Production]),
(tagline:TEXT),
(vote_average:REAL, Examples: [6.5, 8.1, 7.6]),
(vote_count:INTEGER, Examples: [530, 6624, 6122])
]
# Table: movie_genres
[
(movie_id:INTEGER, Examples: [5, 11, 12]),
(genre_id:INTEGER, Examples: [35, 80, 12])
]
【Foreign keys】
production_country.country_id=country.country_id
production_country.movie_id=movie.movie_id
movie_keywords.keyword_id=keyword.keyword_id
movie_keywords.movie_id=movie.movie_id
movie_company.company_id=production_company.company_id
movie_company.movie_id=movie.movie_id
movie_cast.gender_id=gender.gender_id
movie_cast.movie_id=movie.movie_id
movie_cast.person_id=person.person_id
movie_crew.department_id=department.department_id
movie_crew.movie_id=movie.movie_id
movie_crew.person_id=person.person_id
movie_languages.language_id=language.language_id
movie_languages.movie_id=movie.movie_id
movie_languages.language_role_id=language_role.role_id
movie_genres.genre_id=genre.genre_id
movie_genres.movie_id=movie.movie_id

    【Question】
    Among the movie in which Dariusz Wolski works as the director of photography, what is the percentage of those movie whose vote average is over 5.0? director of photography refers to job = 'Director of Photography'; vote average is over 8.0 refers to vote_average > 5; percentage = divide(sum(movie_id) when vote_average > 5, count(movie_id)) as percentage

    【Evidence】
    director of photography refers to job = 'Director of Photography'; vote average is over 8.0 refers to vote_average > 5; percentage = divide(sum(movie_id) when vote_average > 5, count(movie_id)) as percentage

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

SQL: SELECT CAST(COUNT(CASE WHEN T1.vote_average > 5 THEN 1 ELSE NULL END) AS REAL) * 100 / COUNT(T1.vote_average) FROM movie AS T1 INNER JOIN movie_crew AS T2 ON T1.movie_id = T2.movie_id INNER JOIN person AS T3 ON T2.person_id = T3.person_id WHERE T3.person_name = 'Dariusz Wolski' AND T2.job = 'Director of Photography'