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"content": "```json\n[\n {\n \"Name\": \"case_enquiry_id\",\n \"Type\": \"Integer\",\n \"Label\": \"Case Enquiry ID\",\n \"Description\": \"Unique numeric identifier for each case enquiry (100 unique values). The field is non‑nullable, with a mean of 101,004,116.4518 and a standard deviation of 790.552. Values range from 101,004,113,298 to 101,004,155,594.\"\n },\n {\n \"Name\": \"open_dt\",\n \"Type\": \"String\",\n \"Label\": \"Open Date Time\",\n \"Description\": \"Timestamp when the case was opened (01‑Jan‑2022 00:16:00 to 31‑Jan‑2022 11:46:00). All 100 values are unique; there are no nulls.\"\n },\n {\n \"Name\": \"target_dt\",\n \"Type\": \"String\",\n \"Label\": \"Target Date Time\",\n \"Description\": \"Planned or expected resolution timestamp. Contains 32 distinct dates (25 % frequency for the most common date) plus a NULL value that occurs 11 times (11 %). The overall distribution is highly skewed with an IQR of 1,686 seconds.\"\n },\n {\n \"Name\": \"closed_dt\",\n \"Type\": \"String\",\n \"Label\": \"Closed Date Time\",\n \"Description\": \"Actual resolution timestamp. 76 distinct values plus a NULL value (15 % frequency). The dates range from 01‑Jan‑2022 12:56:14 to 25‑Apr‑2022 14:30:31.\"\n },\n {\n \"Name\": \"ontime\",\n \"Type\": \"String\",\n \"Label\": \"On‑Time Status\",\n \"Description\": \"Binary flag indicating whether the case closed on time. 83 rows are marked ONTIME (83 %) and 17 as OVERDUE (17 %). No nulls.\"\n },\n {\n \"Name\": \"case_status\",\n \"Type\": \"String\",\n \"Label\": \"Case Status\",\n \"Description\": \"Current status of the case: CLOSED (85 %) or OPEN (15 %).\"\n },\n {\n \"Name\": \"closure_reason\",\n \"Type\": \"String\",\n \"Label\": \"Closure Reason\",\n \"Description\": \"Narrative description of why a case was closed. The field has 76 distinct reasons plus a NULL value that accounts for 15 % of the rows.\"\n },\n {\n \"Name\": \"case_title\",\n \"Type\": \"String\",\n \"Label\": \"Case Title\",\n \"Description\": \"Short title summarizing the issue (e.g., \\\"Parking Enforcement\\\"). 32 distinct titles; 41 % are marked as OTHER.\"\n },\n {\n \"Name\": \"subject\",\n \"Type\": \"String\",\n \"Label\": \"Subject\",\n \"Description\": \"Department or agency that received the request. The most frequent values are Public Works Department (51 %) and Transportation‑Traffic Division (28 %).\"\n },\n {\n \"Name\": \"reason\",\n \"Type\": \"String\",\n \"Label\": \"Reason\",\n \"Description\": \"Root cause or category of the issue (e.g., Enforcement & Abandoned Vehicles). 10 distinct reasons plus OTHER (13 %).\"\n },\n {\n \"Name\": \"type\",\n \"Type\": \"String\",\n \"Label\": \"Case Type\",\n \"Description\": \"Specific classification of the case. The most common type is Parking Enforcement (20 %) followed by CE Collection (8 %).\"\n },\n {\n \"Name\": \"queue\",\n \"Type\": \"String\",\n \"Label\": \"Queue\",\n \"Description\": \"Internal routing queue for the case. BTDT_Parking Enforcement leads with 21 % of rows; OTHER accounts for 37 %.\"\n },\n {\n \"Name\": \"department\",\n \"Type\": \"String\",\n \"Label\": \"Department\",\n \"Description\": \"Primary department handling the case (e.g., PWDx, BTDT). PWDx has the largest share at 49 %.\"\n },\n {\n \"Name\": \"submittedphoto\",\n \"Type\": \"String\",\n \"Label\": \"Submitted Photo URL\",\n \"Description\": \"Link to an uploaded photo. 58 % of rows contain a NULL value; 33 % have URLs, with the remaining 9 % being unique links.\"\n },\n {\n \"Name\": \"closedphoto\",\n \"Type\": \"String\",\n \"Label\": \"Closed Photo URL\",\n \"Description\": \"Link to a photo taken when the case was closed. All rows are NULL (100 %).\"\n },\n {\n \"Name\": \"location\",\n \"Type\": \"String\",\n \"Label\": \"Location\",\n \"Description\": \"Address or intersection where the issue occurred. 88 distinct values plus a NULL value that accounts for 1 % of the dataset.\"\n },\n {\n \"Name\": \"fire_district\",\n \"Type\": \"String\",\n \"Label\": \"Fire District\",\n \"Description\": \"Boston fire district number (e.g., 3, 4). The most common district is 3 (19 %).\"\n },\n {\n \"Name\": \"pwd_district\",\n \"Type\": \"String\",\n \"Label\": \"Parks and Recreation District\",\n \"Description\": \"PWD district identifier. 1B and 1C are the top two districts with 16 % each.\"\n },\n {\n \"Name\": \"city_council_district\",\n \"Type\": \"String\",\n \"Label\": \"City Council District\",\n \"Description\": \"Boston city council district number. District 1 dominates (22 %).\"\n },\n {\n \"Name\": \"police_district\",\n \"Type\": \"String\",\n \"Label\": \"Police District\",\n \"Description\": \"Boston police precinct (e.g., A1, D4). A1 is the most frequent district at 20 %.\"\n },\n {\n \"Name\": \"neighborhood\",\n \"Type\": \"String\",\n \"Label\": \"Neighborhood\",\n \"Description\": \"Geographic neighborhood of the incident. Dorchester leads with 15 %; OTHER represents 19 %.\"\n },\n {\n \"Name\": \"neighborhood_services_district\",\n \"Type\": \"String\",\n \"Label\": \"Neighborhood Services District\",\n \"Description\": \"Local services district (e.g., 3, 5). District 3 is the most common (15 %).\"\n },\n {\n \"Name\": \"ward\",\n \"Type\": \"String\",\n \"Label\": \"Ward\",\n \"Description\": \"Boston ward number. Ward 3 is the largest group (10 %); OTHER covers 45 %.\"\n },\n {\n \"Name\": \"precinct\",\n \"Type\": \"String\",\n \"Label\": \"Precinct\",\n \"Description\": \"Precinct identifier (e.g., 0306). Precinct 0306 has 5 %; OTHER accounts for 71 %.\"\n },\n {\n \"Name\": \"location_street_name\",\n \"Type\": \"String\",\n \"Label\": \"Street Name\",\n \"Description\": \"Primary street or intersection. The field contains 87 distinct values plus a NULL value that is 4 % of the rows.\"\n },\n {\n \"Name\": \"location_zipcode\",\n \"Type\": \"String\",\n \"Label\": \"Zip Code\",\n \"Description\": \"ZIP code of the incident location. 14 distinct ZIP codes, with OTHER covering 30 %.\"\n },\n {\n \"Name\": \"latitude\",\n \"Type\": \"Float\",\n \"Label\": \"Latitude\",\n \"Description\": \"Geographic latitude coordinate. The most common value is 42.3594 (20 %). Values range from 42.2553 to 42.3806.\"\n },\n {\n \"Name\": \"longitude\",\n \"Type\": \"Float\",\n \"Label\": \"Longitude\",\n \"Description\": \"Geographic longitude coordinate. The most frequent longitude is –71.0587 (19 %). Range spans –71.1626 to –71.0298.\"\n },\n {\n \"Name\": \"source\",\n \"Type\": \"String\",\n \"Label\": \"Source\",\n \"Description\": \"Origin of the request (e.g., Citizens Connect App). The app is responsible for 56 % of cases; other sources are much less common.\"\n }\n]\n```",
"reasoning": "Need JSON array of objects with Name, Type, Label, Description. Use summary stats for type and label maybe same as field name. Provide description using stats like cardinality, uniqueness, nullcount etc.",
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