Initial commit - WhyRating Engine (Google Reviews Scraper)
This commit is contained in:
Alejandro Gutiérrez
411
migrations/versions/013_insert_primitives.sql
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411
migrations/versions/013_insert_primitives.sql
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-- Migration: Insert frozen primitive dictionary (36 primitives)
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-- Description: Populates the primitives table with the complete URT taxonomy
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-- Date: 2025-01-31
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-- Quality dimension (8 primitives)
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'EFFECTIVENESS',
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'QUALITY',
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'Effectiveness',
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'Did it achieve its intended purpose?',
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FALSE,
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'["worked perfectly", "exactly what I needed", "solved my problem"]',
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'["didn''t work", "useless", "waste of time"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'TASTE',
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'QUALITY',
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'Taste',
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'Sensory quality (flavor, texture, smell)',
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FALSE,
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'["delicious", "amazing taste", "flavorful"]',
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'["bland", "tasteless", "disgusting"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'CRAFT',
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'QUALITY',
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'Craft',
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'Skill of execution, workmanship',
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FALSE,
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'["well-made", "professional", "quality work"]',
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'["sloppy", "poorly made", "amateur"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'ACCURACY',
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'QUALITY',
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'Accuracy',
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'Correct as ordered/specified',
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FALSE,
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'["exactly what I ordered", "perfect", "got everything right"]',
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'["wrong order", "missing items", "not what I asked for"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'FRESHNESS',
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'QUALITY',
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'Freshness',
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'Fresh vs stale/expired',
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FALSE,
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'["fresh", "just made", "new"]',
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'["stale", "old", "expired"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'TEMPERATURE',
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'QUALITY',
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'Temperature',
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'Appropriate temperature for the item',
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FALSE,
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'["hot", "perfect temperature", "cold as it should be"]',
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'["cold", "lukewarm", "too hot", "room temperature"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'CONDITION',
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'QUALITY',
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'Condition',
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'Physical state, damage, defects',
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FALSE,
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'["perfect condition", "like new", "undamaged"]',
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'["damaged", "broken", "defective"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'CONSISTENCY',
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'QUALITY',
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'Consistency',
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'Same quality across visits/units',
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FALSE,
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'["always consistent", "reliable quality", "same every time"]',
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'["inconsistent", "hit or miss", "varies"]'
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);
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-- Service dimension (4 primitives)
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'MANNER',
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'SERVICE',
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'Manner',
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'Warmth, respect, patience in interactions',
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FALSE,
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'["friendly", "nice", "welcoming", "patient"]',
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'["rude", "dismissive", "impatient", "attitude"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'COMPETENCE',
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'SERVICE',
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'Competence',
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'Knowledge and skill of staff',
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FALSE,
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'["knowledgeable", "professional", "knew what they were doing"]',
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'["clueless", "incompetent", "didn''t know"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'ATTENTIVENESS',
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'SERVICE',
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'Attentiveness',
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'Present, notices needs, proactive',
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FALSE,
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'["attentive", "checked on us", "anticipated needs"]',
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'["ignored", "had to flag down", "neglected"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'COMMUNICATION',
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'SERVICE',
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'Communication',
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'Clear, listens, keeps informed',
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FALSE,
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'["clear", "good communication", "kept us updated"]',
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'["confusing", "didn''t listen", "no updates"]'
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);
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-- Process dimension (4 primitives)
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'SPEED',
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'PROCESS',
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'Speed',
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'How fast/slow things happen',
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FALSE,
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'["fast", "quick", "no wait"]',
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'["slow", "took forever", "long wait"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'FRICTION',
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'PROCESS',
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'Friction',
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'Ease vs obstacles in the process',
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FALSE,
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'["easy", "smooth", "hassle-free"]',
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'["complicated", "difficult", "hassle"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'RELIABILITY',
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'PROCESS',
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'Reliability',
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'Process works consistently, no errors',
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FALSE,
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'["reliable", "dependable", "always works"]',
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'["unreliable", "errors", "problems"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'AVAILABILITY',
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'PROCESS',
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'Availability',
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'Hours, capacity, stock availability',
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FALSE,
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'["always available", "open when needed", "in stock"]',
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'["closed", "sold out", "no appointments"]'
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);
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-- Environment dimension (6 primitives)
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'CLEANLINESS',
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'ENVIRONMENT',
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'Cleanliness',
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'Clean, sanitary conditions',
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FALSE,
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'["clean", "spotless", "hygienic"]',
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'["dirty", "filthy", "unsanitary"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'COMFORT',
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'ENVIRONMENT',
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'Comfort',
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'Physical comfort of the space',
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FALSE,
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'["comfortable", "cozy", "spacious"]',
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'["uncomfortable", "cramped", "hard seats"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'AMBIANCE',
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'ENVIRONMENT',
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'Ambiance',
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'Vibe, atmosphere, noise level',
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FALSE,
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'["nice atmosphere", "great vibe", "quiet"]',
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'["loud", "noisy", "bad atmosphere"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'SAFETY',
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'ENVIRONMENT',
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'Safety',
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'Physical and health safety',
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FALSE,
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'["safe", "secure", "clean protocols"]',
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'["unsafe", "dangerous", "health hazard"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'ACCESSIBILITY',
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'ENVIRONMENT',
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'Accessibility',
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'Disability access, location convenience',
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FALSE,
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'["accessible", "easy to get to", "good parking"]',
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'["hard to access", "no parking", "not wheelchair accessible"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'DIGITAL_UX',
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'ENVIRONMENT',
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'Digital UX',
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'App/website usability and performance',
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FALSE,
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'["easy to use", "great app", "fast website"]',
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'["app crashed", "hard to navigate", "slow website"]'
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);
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-- Value dimension (4 primitives)
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'PRICE_LEVEL',
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'VALUE',
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'Price Level',
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'Absolute cost perception',
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FALSE,
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'["affordable", "cheap", "good prices"]',
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'["expensive", "overpriced", "pricey"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'PRICE_FAIRNESS',
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'VALUE',
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'Price Fairness',
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'Fair value for what was received',
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FALSE,
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'["fair price", "worth it", "good value"]',
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'["rip off", "not worth it", "overcharged"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'PRICE_TRANSPARENCY',
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'VALUE',
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'Price Transparency',
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'Clear pricing, no surprises',
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FALSE,
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'["clear pricing", "no hidden fees", "upfront"]',
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'["hidden fees", "surprise charges", "bait and switch"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'VALUE_FOR_MONEY',
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'VALUE',
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'Value for Money',
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'Overall worth judgment',
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FALSE,
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'["great value", "worth every penny", "good deal"]',
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'["bad value", "waste of money", "not worth it"]'
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);
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-- Trust dimension (3 meta primitives)
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'HONESTY',
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'TRUST',
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'Honesty',
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'Truthful, no deception',
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TRUE,
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'["honest", "transparent", "truthful"]',
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'["lied", "deceived", "dishonest", "scam"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'ETHICS',
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'TRUST',
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'Ethics',
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'Ethical, fair dealing',
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TRUE,
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'["ethical", "fair", "integrity"]',
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'["unethical", "shady", "crooked"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'PROMISES',
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'TRUST',
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'Promises',
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'Kept or broken commitments',
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TRUE,
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'["kept their word", "delivered as promised", "reliable"]',
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'["broke promise", "didn''t deliver", "false advertising"]'
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);
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-- Resolution dimension (3 meta primitives)
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'ACKNOWLEDGMENT',
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'RESOLUTION',
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'Acknowledgment',
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'Recognized the problem',
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TRUE,
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'["acknowledged", "apologized", "admitted mistake"]',
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'["denied", "dismissed", "blamed me"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'RESPONSE_QUALITY',
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'RESOLUTION',
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'Response Quality',
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'How well they handled the issue',
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TRUE,
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'["handled well", "resolved quickly", "took care of it"]',
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'["ignored complaint", "unhelpful", "made it worse"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'RECOVERY',
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'RESOLUTION',
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'Recovery',
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'Made it right, compensation',
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TRUE,
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'["made it right", "refunded", "compensated"]',
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'["refused refund", "no compensation", "wouldn''t fix"]'
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);
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-- Loyalty dimension (3 meta primitives)
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'RETURN_INTENT',
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'LOYALTY',
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'Return Intent',
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'Will/won''t come back',
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TRUE,
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'["will be back", "returning customer", "coming again"]',
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'["never again", "won''t return", "last time"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'RECOMMEND',
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'LOYALTY',
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'Recommend',
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'Would/wouldn''t recommend',
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TRUE,
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'["highly recommend", "tell everyone", "must try"]',
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'["avoid", "don''t go", "stay away", "wouldn''t recommend"]'
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);
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'RECOGNITION',
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'LOYALTY',
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'Recognition',
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'Felt valued, remembered',
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TRUE,
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'["remembered me", "felt valued", "personal touch"]',
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'["treated like a number", "didn''t care", "no loyalty"]'
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);
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-- Escape dimension (1 meta primitive)
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INSERT INTO pipeline.primitives (code, dimension, name, definition, is_meta, base_positive_signals, base_negative_signals)
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VALUES (
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'UNMAPPED',
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'ESCAPE',
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'Unmapped',
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'Does not fit taxonomy; preserve evidence',
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TRUE,
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'[]',
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'[]'
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);
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-- Verify count
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-- SELECT COUNT(*) FROM pipeline.primitives; -- Should return 36
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