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

    【Schema】
    【DB_ID】 synthea
【Schema】
# Table: allergies
[
(START:TEXT, Examples: [3/11/95, 6/15/58, 11/14/12]),
(STOP:TEXT, Examples: [12/22/14, 4/21/10, 10/15/11]),
(PATIENT:TEXT, Primary Key, Examples: [00341a88-1cc1-4b39-b0f9-05b0531991a0]),
(ENCOUNTER:TEXT, Primary Key, Examples: [ddc8a625-07e6-4a02-a616-e0ce07b5f305]),
(CODE:INTEGER, Primary Key, Examples: [232347008, 300916003, 419474003]),
(DESCRIPTION:TEXT, Examples: [Allergy to dairy product])
]
# Table: encounters
[
(ID:TEXT, Primary Key, Examples: [000c20ea-5c3d-43a3-9608-bd37c22f13c8]),
(DATE:DATE, Examples: [2008-03-11]),
(PATIENT:TEXT, Examples: [71949668-1c2e-43ae-ab0a-64654608defb]),
(CODE:INTEGER, Examples: [185349003, 185345009, 698314001]),
(DESCRIPTION:TEXT, Examples: [Outpatient Encounter]),
(REASONCODE:INTEGER, Examples: [10509002, 38822007, 72892002]),
(REASONDESCRIPTION:TEXT, Examples: [Acute bronchitis (disorder)])
]
# Table: all_prevalences
[
(ITEM:TEXT, Primary Key),
(POPULATION TYPE:TEXT, Examples: [LIVING]),
(OCCURRENCES:INTEGER, Examples: [868, 487, 117]),
(POPULATION COUNT:INTEGER, Examples: [1000]),
(PREVALENCE RATE:REAL, Examples: [0.868, 0.487, 0.117]),
(PREVALENCE PERCENTAGE:REAL, Examples: [86.8, 48.7, 11.7])
]
# Table: patients
[
(patient:TEXT, Primary Key, Examples: [00269bb7-e3ab-43a9-9cdf-cdf9b6e3b2b3]),
(birthdate:DATE, Examples: [1929-04-08]),
(deathdate:DATE, Examples: [2029-11-11]),
(ssn:TEXT, Examples: [999-78-5976, 999-59-9186, 999-43-3780]),
(drivers:TEXT, Examples: [S99992928, S99975961, S99997003]),
(passport:TEXT, Examples: [FALSE, X98167138X, X65866752X]),
(prefix:TEXT, Examples: [Mr., Mrs., Ms.]),
(first:TEXT, Examples: [Rosamaria, Loan, Angelo]),
(last:TEXT, Examples: [Pfannerstill, Bashirian, Buckridge]),
(suffix:TEXT, Examples: [PhD, JD, MD]),
(maiden:TEXT, Examples: [Schuster, Schaden, Brekke]),
(marital:TEXT, Examples: [S, M]),
(race:TEXT, Examples: [black, white, hispanic]),
(ethnicity:TEXT, Examples: [dominican, american, african]),
(gender:TEXT, Examples: [F, M]),
(birthplace:TEXT, Examples: [Pittsfield MA US, Medford MA US, Framingham MA US]),
(address:TEXT)
]
# Table: medications
[
(START:DATE, Primary Key, Examples: [1918-04-17]),
(STOP:DATE, Examples: [2008-06-04]),
(PATIENT:TEXT, Primary Key, Examples: [ce11bcba-c83c-43ae-802d-b20ee8715afe]),
(ENCOUNTER:TEXT, Primary Key, Examples: [6334071d-e6b0-42c1-a082-2a08e123de4e]),
(CODE:INTEGER, Primary Key, Examples: [834060, 745679, 895994]),
(DESCRIPTION:TEXT),
(REASONCODE:INTEGER, Examples: [43878008, 10509002, 38822007]),
(REASONDESCRIPTION:TEXT, Examples: [Streptococcal sore throat (disorder)])
]
# Table: procedures
[
(DATE:DATE, Examples: [2013-02-09]),
(PATIENT:TEXT, Examples: [71949668-1c2e-43ae-ab0a-64654608defb]),
(ENCOUNTER:TEXT, Examples: [6f2e3935-b203-493e-a9c0-f23e847b9798]),
(CODE:INTEGER, Examples: [23426006, 252160004, 237001001]),
(DESCRIPTION:TEXT, Examples: [Measurement of respiratory function (procedure)]),
(REASONCODE:INTEGER, Examples: [10509002, 72892002, 33737001]),
(REASONDESCRIPTION:TEXT, Examples: [Acute bronchitis (disorder)])
]
# Table: claims
[
(ID:TEXT, Primary Key, Examples: [0004caaf-fd7a-4f64-bda6-dce5ebd1196c]),
(PATIENT:TEXT, Examples: [71949668-1c2e-43ae-ab0a-64654608defb]),
(BILLABLEPERIOD:DATE, Examples: [2008-03-11]),
(ORGANIZATION:TEXT, Examples: [temp organization]),
(ENCOUNTER:TEXT, Examples: [71949668-1c2e-43ae-ab0a-64654608defb]),
(DIAGNOSIS:TEXT),
(TOTAL:INTEGER, Examples: [100])
]
# Table: observations
[
(DATE:DATE, Examples: [2008-03-11]),
(PATIENT:TEXT, Examples: [71949668-1c2e-43ae-ab0a-64654608defb]),
(ENCOUNTER:TEXT, Examples: [5114a5b4-64b8-47b2-82a6-0ce24aae0943]),
(CODE:TEXT, Examples: [8302-2, 29463-7, 39156-5]),
(DESCRIPTION:TEXT, Examples: [Body Height, Body Weight, Body Mass Index]),
(VALUE:REAL, Examples: [166.03, 54.42, 19.74]),
(UNITS:TEXT, Examples: [cm, kg, kg/m2])
]
# Table: careplans
[
(ID:TEXT, Examples: [e031962d-d13d-4ede-a449-040417d5e4fb]),
(START:DATE, Examples: [2009-01-11]),
(STOP:DATE, Examples: [2009-04-07]),
(PATIENT:TEXT, Examples: [71949668-1c2e-43ae-ab0a-64654608defb]),
(ENCOUNTER:TEXT, Examples: [4d451e22-a354-40c9-8b33-b6126158666d]),
(CODE:REAL, Examples: [53950000.0, 304510005.0, 371605008.0]),
(DESCRIPTION:TEXT, Examples: [Respiratory therapy]),
(REASONCODE:INTEGER, Examples: [10509002, 38822007, 72892002]),
(REASONDESCRIPTION:TEXT, Examples: [Acute bronchitis (disorder)])
]
# Table: immunizations
[
(DATE:DATE, Primary Key, Examples: [2007-11-02]),
(PATIENT:TEXT, Primary Key, Examples: [70cbc79e-dd9d-4e3b-8158-a92d85951f94]),
(ENCOUNTER:TEXT, Primary Key, Examples: [898ab2de-8a71-4fb3-b239-b768b915284a]),
(CODE:INTEGER, Primary Key, Examples: [8, 62, 140]),
(DESCRIPTION:TEXT, Examples: [Influenza  seasonal  injectable  preservative free])
]
# Table: conditions
[
(START:DATE, Examples: [2009-01-08]),
(STOP:DATE, Examples: [2009-01-21]),
(PATIENT:TEXT, Examples: [71949668-1c2e-43ae-ab0a-64654608defb]),
(ENCOUNTER:TEXT, Examples: [4d451e22-a354-40c9-8b33-b6126158666d]),
(CODE:INTEGER, Examples: [10509002, 38822007, 72892002]),
(DESCRIPTION:TEXT, Examples: [Acute bronchitis (disorder)])
]
【Foreign keys】
allergies.ENCOUNTER=encounters.ID
allergies.PATIENT=patients.patient
encounters.PATIENT=patients.patient
medications.ENCOUNTER=encounters.ID
medications.PATIENT=patients.patient
procedures.ENCOUNTER=encounters.ID
procedures.PATIENT=patients.patient
claims.ENCOUNTER=encounters.ID
claims.PATIENT=patients.patient
observations.ENCOUNTER=encounters.ID
observations.PATIENT=patients.patient
careplans.ENCOUNTER=encounters.ID
careplans.PATIENT=patients.patient
immunizations.ENCOUNTER=encounters.ID
immunizations.PATIENT=patients.patient
conditions.ENCOUNTER=encounters.ID
conditions.PATIENT=patients.patient
conditions.DESCRIPTION=all_prevalences.ITEM

    【Question】
    Provide the number of encounters for Major D'Amore.

    【Evidence】
    

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

SQL: SELECT COUNT(T2.ID) FROM patients AS T1 INNER JOIN encounters AS T2 ON T1.patient = T2.PATIENT WHERE T1.first = 'Major' AND T1.last = 'D''Amore'