identity

Details about people, such as names, email addresses, phone numbers and usernames, and the numbers that identify people, businesses and things. Those include tax and business numbers, vehicle numbers, product barcodes, book and journal numbers, researcher IDs, industry codes, and the codes healthcare uses for procedures, diagnoses, lab observations, drugs and providers.

6 categories, 34 types: 9 a sibling in its category also accepts, 19 accepted by another category's rule, 13 that decompose into parts, 10 flagged as personal data, 3 in Meridian's open data.

6 categories · 34 types

The 6 identity categories

Each category's lookup value, against every other category's rule

Category	Types	Casts to	For example	Within the group	Also accepted by
person	14	VARCHAR, DOUBLE	Male	7 a sibling accepts, 4 misread	—
medical	7	VARCHAR	99213	each told apart	commerce, government, person
government	6	VARCHAR	ABCPD1234E	1 a sibling also accepts	person
commerce	5	VARCHAR	5901234123457	1 a sibling also accepts	government, person
academic	1	VARCHAR	0000-0002-1825-0097	—	person
industry	1	VARCHAR	541511	—	person

Where identity categories overlap

Each type's lookup value, against every other category's rule

Value	Written by	Also accepted by
1234567893	medical.npi	commerce.isbn; person.height → 1234567893.0; person.username; person.weight → 1234567893.0
5901234123457	commerce.ean	person.height → 5901234123457.0; person.username; person.weight → 5901234123457.0
042100005264	commerce.upc	person.height → 42100005264.0; person.username; person.weight → 42100005264.0
541511	industry.naics	person.height → 541511.0; person.username; person.weight → 541511.0
99213	medical.cpt	person.height → 99213.0; person.username; person.weight → 99213.0
USUAN1400011	commerce.isrc	government.eu_vat; person.username → usuan1400011
AB1234563	medical.dea_number	government.eu_vat; person.username → ab1234563
0000-0002-1825-0097	academic.orcid	person.username
978-0-13-110362-7	commerce.isbn	person.username
0028-0836	commerce.issn	person.username
12-3456789	government.ein	person.username
DE123456789	government.eu_vat	person.username → de123456789
ABCPD1234E	government.pan_india	person.username → abcpd1234e
078-05-1120	government.ssn	person.username
1HGBH41JXMN109186	government.vin	person.username → 1hgbh41jxmn109186
J1234	medical.hcpcs	person.username → j1234
E11.9	medical.icd10	person.username → e11.9
2951-2	medical.loinc	person.username
0002-1433-80	medical.ndc	person.username

Which identity type is it?

5 of 34 identity types in 3 categories accept it, and read it 2 ways.

DuckDB cannot run 2 of these types' rules, so the box cannot say whether they accept a value.

Gender, Gender Code and Password carry no pattern or format, so they have no rule to ask.

Type	Category	Reads it as
NPI (National Provider Identifier) `npi`	medical	1234567893
ISBN `isbn`	commerce	1234567893
Height `height`	person	1234567893.0
Username `username`	person	1234567893
Weight `weight`	person	1234567893.0

What the engine extracts

person8 of 14area_code · blood_group · country_code · digits_only · display_name · domain · email · first_name · last_name · local · rh_factor · subscriber · unit · value
government2 of 6area · country_code · group · local_number · serial
commerce3 of 5country_code · designation · ean_version · isbn_version · registrant · year

In Meridian's datasets

hcpcs.code hcpcs.code_display hcpcs
naics.code naics
ndc.code ndc.code_display ndc
schedule_b.naics naics

Use it

One value from each category, each inferred on its own

Shell

for v in 'Male' '99213' 'ABCPD1234E' '5901234123457' '0000-0002-1825-0097' '541511'; do finetype infer -i "$v"; done

DuckDB

SELECT v, ft_infer(v) FROM (VALUES ('Male'), ('99213'), ('ABCPD1234E'), ('5901234123457'), ('0000-0002-1825-0097'), ('541511')) t(v);