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Coverage

46,873 of 56,681 columns decided — 82.7% · 9,148 bound (16.1%) · 9,808 still unread

Bound: 9,148; 16.1%; a concept was found and asserted for the column.
Refused: 29,070; 51.3%; the column was read and no concept fits. A verdict, not a gap.
Insufficient: 8,655; 15.3%; read, but the evidence did not settle it.
Unanalysed: 9,808; 17.3%; nobody has read this column yet.

56,681 columns in the coverage ledger · read from /api/field-coverage on citytap-prod · source last observed Oct 10, 2026, 12:55 PM

Where the unread columns are

3,026 datasets, 56,681 catalogued fields, 9,808 of them still unread. Every band below is a share of that, worst first. Open one to see its datasets.

Citywide Payroll Data (Fiscal Year)

k397-673e

← What we have understood · Dataset page

Fields catalogued

Understood

Checked, no match

Nobody has looked

Every field decided — Every field carries a verdict — which does not mean every field is understood.

Every field in this dataset

Covered and uncovered alike, in the order the publisher lists them. A field with no verdict is one nobody has looked at yet.

Field	Type	What we know	Meaning	How it was decided	Shared term
regular_gross_paid Regular Gross Paid	Number	Understood	quantity.money Monetary amount	dictionary_read amount paid to the employee for base salary	—
leave_status_as_of_june_30 Leave Status as of June 30	Text	Understood	category.status Workflow status	value_profile Status of employee as of the close of the relevant fiscal year: Active, Ceased, or On Leave	—
work_location_borough Work Location Borough	Text	Checked, no match	—	value_domain_read smallest=ALBANY largest=WESTCHESTER top[8 of 20]={MANHATTAN×4480697; QUEENS×683487; BROOKLYN×570420; BRONX×312799; OTHER×120152; RICHMOND×84659; WESTCHESTER×6221; ULSTER×3544}	—
title_description Title Description	Text	Checked, no match	—	value_domain_read smallest=12 MONTH SPECIAL EDUCATION A largest=YOUTH DEVELOPMENT SPECIALIST	—
base_salary Base Salary	Number	Understood	quantity.money Monetary amount	dictionary_read Base Salary assigned to the employee	—
mid_init Mid Init	Text	Understood	actor.person_name Person name	dictionary_read Middle initial of employee	—
ot_hours OT Hours	Number	Checked, no match	—	value_domain_read smallest=-1425.42 largest=3692.9 top[8 of 20]={0×5089854; 1×13564; 2×10891; 8×10847; 4×8750; 3×7794; 5×6753; 7×6400}	—
first_name First Name	Text	Understood	actor.person_name.given Given name	dictionary_read First name of employee	—
pay_basis Pay Basis	Text	Understood	category.classification Publisher classification	dictionary_read Lists whether the employee is paid on an hourly, per diem or annual basis	—
total_ot_paid Total OT Paid	Number	Understood	quantity.money Monetary amount	dictionary_read overtime pay paid to the employee	—
total_other_pay Total Other Pay	Number	Understood	quantity.money Monetary amount	dictionary_read Includes any compensation in addition to gross salary and overtime pay	—
payroll_number Payroll Number	Number	Checked, no match	—	value_domain_read smallest=2 largest=996 top[8 of 20]={742×992432; 747×835132; 56×479852; 744×356297; 300×310674; 745×204517; 57×172604; 740×151281}	—
agency_start_date Agency Start Date	Calendar date	Understood	time.period.month Month	value_profile Date which employee began working for their current agency	—
regular_hours Regular Hours	Number	Checked, no match	—	value_domain_read smallest=-1425.42 largest=3692.9 top[8 of 20]={0×5089854; 1×13564; 2×10891; 8×10847; 4×8750; 3×7794; 5×6753; 7×6400}	—
last_name Last Name	Text	Understood	actor.person_name.family Family name	dictionary_read Last name of employee	—
agency_name Agency Name	Text	Understood	actor.agency City agency	dictionary_read The Payroll agency that the employee works for	agency
fiscal_year Fiscal Year	Number	Understood	time.fiscal_period Fiscal or school period	dictionary_read Fiscal Year	—