dbprint format v1 — vocabulary example

A second, small print directory alongside ../production/, produced by scripts/gen_vocabulary_example.py against a PostgreSQL database seeded from scripts/sql/, and regenerated with just example-vocabulary. It exists for one reason: seventeen looks_like patterns have no honest column in the seed-bank domain production/ demonstrates — a bank card number, an IBAN, a MAC address are not things a seed bank's schema would ever hold — so this tree carries a single table built to demonstrate exactly those seventeen, plus one already shown in production/ for a real primary key. Beyond looks_like, this tree also demonstrates the inferred.epoch_unit field's per-value evidence rule, and nine sensitivity categories: national_id, date_of_birth, health, demographic and employment each get their own column, and financial_account/online_identifier/geolocation/credential need none of their own, since they corroborate from shapes pan/iban_value/mac_address/device_id/coordinates/bearer_token already carry - see below.

Same golden-comparison discipline as production/: tests/conformance/test_reference_example.py regenerates the tree against a fresh database and compares it byte-for-byte against the committed one, so what is here is what the producer emits, not a hand-edited fixture.

Source

One schema, one table: public.shapes, 40 rows, one column per demonstrated pattern plus its row_id primary key. Every value is a published test vector, a standard registry entry, or an invented placeholder — never framed as data observed in the wild: Luhn-valid non-issuable card numbers, the documentation IBANs ISO 13616 and Wikipedia's own worked examples use, RFC 4122's example UUID inside a URN, locally-administered MAC addresses (RFC 7042's documentation range), IANA zone names, ISO 4217 currency codes, and hand-built JWTs carrying no real signing key, semantic version strings in their tag-prefixed and prerelease/build forms, published ISBN/EAN/UPC test identifiers, VINs satisfying the federal VIN regulation's own check digit (49 C.F.R. 565.15), IMEIs whose Reporting Body Identifier is genuinely allocated (GSMA's own IMEI allocation guidelines), and epoch-second instants chosen to avoid isbn's own check digit by coincidence, invented tax-reference numbers naming no real jurisdiction's scheme, invented birth dates, standard ABO/Rh blood-type codes, and abstract placeholder group labels — never a real ethnicity, religion or other protected-category name. Each column cycles three such values across its 40 rows, which is why every column classifies categorical with cardinality: 3 and an exhaustive, values_coverage: 1.0 value list — the small-fixture case production/ demonstrates the classification mechanics of at greater length; this tree only needs a categorical column to carry each pattern's value.

What's demonstrated

Column	looks_like	Values
row_id	numeric_string	Row ordinal — the same pattern production/ demonstrates on every surrogate key
pan	card_number	Three Luhn-valid, non-issuable test card numbers - also sensitivity: financial_account, from the shape alone (see below)
iban_value	iban	ISO 13616 documentation IBANs (Germany, UK, France), check digits valid on all three - also sensitivity: financial_account, from the shape alone (see below)
bic_value	bic	An 8-character and an 11-character (branch-suffixed) BIC, both valid shapes
digest	hex	Two #-prefixed hex triplets and one bare 7-character hex string — SPEC 4.1's #-prefix is optional, not required
mac_address	mac_address	Colon- and hyphen-delimited forms, both locally-administered (RFC 7042)
coordinates	latlon	A comma-paired lat/lon in three different sign/precision combinations, including 0.0,0.0
resource_urn	urn	Three RFC 8141 URNs across different namespaces (isbn, uuid, an invented example namespace)
duration	iso8601_duration	A full six-component duration, a minutes-only duration, and a weeks-only duration — the three component groupings ISO 8601 allows
tz_name	timezone	Two IANA zone names and UTC, all present in the tzdata snapshot the producer resolves against
currency	currency_code	Three active ISO 4217 alpha codes
bearer_token	jwt	Three HS256-shaped tokens with distinct payloads, each a real base64url-encoded JOSE header carrying alg
package_version	semver	A tag-prefixed release, a numeric prerelease, and an alphabetic prerelease-plus-build form - the three arms this pattern was added to cover
book_code	isbn	A 13-digit ISBN, a 10-digit ISBN, and a 10-digit ISBN with an X check character
barcode	ean	Two 13-digit EANs (one also Luhn-valid - the worked collision isbn/ean outranking card_number exists for) and a UPC-A
vehicle_id	vin	Three VINs valid under the federal VIN regulation (49 C.F.R. 565.15), one with an X check digit
device_id	imei	Three Luhn-valid IMEIs with allocated Reporting Body Identifiers, one inside JCB's card-issuer range - the value a card_number IIN table would still misclaim
logged_at	iso8601_datetime	Three ISO 8601 datetime strings, distinct from date_of_birth's bare-date shape below - a pattern no production/ column reaches, since every native timestamp there classifies temporal and SPEC 4.1.5 runs no detection on that classification

Every value above was checked individually against detect() before being written into the seed data, so each row exercises the same pattern the table claims.

Also demonstrated: inferred.epoch_unit

Column	inferred.epoch_unit	Values
event_timestamp	seconds	Three consecutive epoch-second instants, chosen to avoid isbn's own check digit - each still reports looks_like: numeric_string unchanged

This is epoch_unit's per-value evidence rule (SPEC 4.5.2), which reaches categorical the same way looks_like does. The rule's other half - the bounds rule over a numeric column's range - needs a cardinality above the enumeration threshold (50), which this table's 40 rows cannot carry without breaking every other column's cardinality: 3 demonstration; it is proven instead by the engine test suite (tests/engine/test_orchestrator.py::TestEpochUnit).

Also demonstrated: sensitivity: national_id

Column	inferred.sensitivity	Values
tax_id	national_id	Three invented tax-reference numbers - the column name alone is unambiguous, so no value shape is required

tax_id is a strong national_id token (SPEC 4.4.1): the category is carried from the column name alone, the same treatment first_name gets from personal_name.

Also demonstrated: sensitivity: date_of_birth

Column	inferred.sensitivity	Values
date_of_birth	date_of_birth	Three invented birth dates - the column name alone is unambiguous, so no value shape is required

date_of_birth is a strong date_of_birth token (SPEC 4.4.1), the same unambiguous-name treatment tax_id gets from national_id above. The column also reports looks_like: iso8601_date, since its values are ISO date strings - the two axes are independent (SPEC 4.4's opening paragraph): sensitivity and looks_like are not alternatives.

Also demonstrated: sensitivity: employment

Column	inferred.sensitivity	Values
annual_salary	employment	Three invented salary figures, stored as text - the column name alone is unambiguous, so no value shape is required

annual_salary is a strong employment token (SPEC 4.4.1), the same unambiguous-name treatment tax_id and date_of_birth get. It also reports looks_like: numeric_string, since its values are digit-only strings - the classification does not matter to this axis, only the name does.

Also demonstrated: sensitivity: health and sensitivity: demographic

Column	inferred.sensitivity	Values
blood_type	health	Three ABO/Rh blood-type codes - the column name alone is unambiguous
ethnicity	demographic	Three abstract placeholder group labels, never a real ethnicity name - the column name alone is unambiguous

blood_type and ethnicity are both strong tokens on their own axis (SPEC 4.4.1). The two categories are separate values, not one merged value: detect() never returns both for one column, and RedactRule.covers() targets by exact value membership - so a redact rule naming only health can never reach an ethnicity-flagged column. Category independence is asserted directly by tests/spec/test_sensitivity.py::TestDemographic's test_a_health_only_rule_leaves_demographic_columns_reachable; the redaction primitive itself (drop removes literals, keeps counts) is the generic mechanism tests/engine/test_redaction.py already proves for every category.

Also demonstrated: sensitivity: financial_account

Column	inferred.sensitivity	Why
pan	financial_account	Its own looks_like: card_number corroborates - a Luhn check under the value, not a name heuristic
iban_value	financial_account	Its own looks_like: iban corroborates - a mod-97 check under the value

Neither column's name (pan, iban_value) is a strong financial_account token; both are flagged purely because their values carry a checksummed shape, the same mechanism SPEC 4.4.3 already gives contact for email/phone - the one category on this axis whose value corroboration is arithmetic rather than a heuristic (SPEC 4.4.1).

Also demonstrated: sensitivity: online_identifier

Column	inferred.sensitivity	Why
mac_address	online_identifier	Both a strong name token and its own looks_like: mac_address corroborates
device_id	online_identifier	A strong name token; its own looks_like: imei is unrelated evidence on the other axis

Both columns carry a looks_like verdict as well, so one fixture value demonstrates both axes. mac_address demonstrates the shape-reading path SPEC 4.4.3 gives this category (the same mechanism contact and financial_account use); device_id demonstrates the name-only path. A session_token column is deliberately not demonstrated here - it is a sensitivity: credential example instead, since a live session token is a bearer credential first and the two vocabularies do not claim the same column.

Also demonstrated: sensitivity: geolocation

Column	inferred.sensitivity	Why
coordinates	geolocation	Its own looks_like: latlon corroborates - the column name carries no geolocation token

coordinates carries a looks_like verdict as well, so one fixture value demonstrates both axes. This is the shape-only path - a latitude/longitude pair split across two numeric columns would instead be the name-only path, which this fixture cannot carry (no sample is drawn for a numeric column, so no shape evidence exists there either way) - see "Not demonstrated" below for where that path is covered.

Also demonstrated: sensitivity: credential

Column	inferred.sensitivity	Why
bearer_token	credential	Its own looks_like: jwt corroborates unconditionally - the column name carries no credential token

bearer_token carries a looks_like verdict as well, so one fixture value demonstrates both axes. This is the unconditional shape path (jwt flags independent of the name, the same mechanism contact gets from email/phone) - see "Not demonstrated" below for the strong-name path, the weak-name-plus-hex path, and the settings-table negative.

Not demonstrated

Everything production/ already covers on its own: classifications other than categorical, distributions other than uniform, redaction, relationships, per-table freshness overrides, description.md/statistics.annotations.yaml, the other 16 looks_like patterns — see ../README.md for those — epoch_unit's bounds rule, per the note above — national_id's weak-tier corroboration, which tests/spec/test_sensitivity.py::TestNationalId covers directly — financial_account's weak tier (a generic account_number column corroborated by shape), covered the same way by tests/spec/test_sensitivity.py::TestFinancialAccount — the age exclusion from date_of_birth, covered by tests/spec/test_sensitivity.py::TestDateOfBirth — the health/demographic exclusions (condition, treatment, bare gender/sex, ...), covered by tests/spec/test_sensitivity.py::TestHealth and ::TestDemographic — online_identifier's internal-key/uuid/username exclusions, covered by tests/spec/test_sensitivity.py::TestOnlineIdentifier — geolocation's name-only path plus its city/postcode exclusions, covered by tests/spec/test_sensitivity.py::TestGeolocation — credential's strong-name path, its weak-name-plus-hex path, and the settings-table/checksum-column negatives, covered by tests/spec/test_sensitivity.py::TestCredential — and employment's adjacent-HR-column and ordinary-money exclusions, covered by tests/spec/test_sensitivity.py::TestEmployment.

Conformance

Validating this directory MUST return zero error-severity issues.