Grants paid by Hall Fred UW Fbo Ynhh
EIN 06-6334735 · Dallas, RI · Form 990-PF, Part XV
Hall Fred UW Fbo Ynhh of Dallas, RI (EIN 06-6334735) reported 8 grants paid totaling $120,108 to 1 recipient in tax years 2018–2025, on Form 990-PF, Part XV. Derived from IRS e-file data, dataset version 2026.09.0, built 2026-09-03.
Grants paid
$120,108
8 grants
Recipients
1
distinct organizations
Tax years
2018–2025
8 filings in the corpus
By tax year
| Tax year | Grants paid | Amount paid | Approved for future |
|---|---|---|---|
| 2018 | 1 | $13,248 | — |
| 2019 | 1 | $11,473 | — |
| 2020 | 1 | $13,522 | — |
| 2021 | 1 | $15,158 | — |
| 2022 | 1 | $18,491 | — |
| 2023 | 1 | $12,752 | — |
| 2024 | 1 | $17,552 | — |
| 2025 | 1 | $17,912 | — |
Top recipients
The 1 recipients receiving the most from Hall Fred UW Fbo Ynhh, by grants paid, out of 1 distinct recipient.
| Recipient | Match | Location | Grants | Total paid | Latest year |
|---|---|---|---|---|---|
| Yale New Haven Hospital 06-0646652 | B | New Haven, CT | 8 | $120,108 | 2025 |
Match tier: A Reported EIN · B Exact name and place · C Strong name match · D Probable name match · U Unresolved. Tiers C and D are inferred, not reported; see how matching works.
Every grant
All 8 grant rows this organization reported, most recent first.
| Tax year | Recipient | Match | Amount | Type | Purpose | Source filing |
|---|---|---|---|---|---|---|
| 2025 | Yale New Haven Hospital New Haven, CT | B | $17,912 | paid | UNRESTRICTED GENERAL | 202621119349101742 |
| 2024 | Yale New Haven Hospital New Haven, CT | B | $17,552 | paid | UNRESTRICTED GENERAL | 202540799349100549 |
| 2023 | Yale New Haven Hospital New Haven, CT | B | $12,752 | paid | UNRESTRICTED GENERAL | 202431079349102208 |
| 2022 | Yale New Haven Hospital New Haven, CT | B | $18,491 | paid | UNRESTRICTED GENERAL | 202311019349100426 |
| 2021 | Yale New Haven Hospital New Haven, CT | B | $15,158 | paid | UNRESTRICTED GENERAL | 202240819349100829 |
| 2020 | Yale New Haven Hospital New Haven, CT | B | $13,522 | paid | UNRESTRICTED GENERAL | 202121059349101602 |
| 2019 | Yale New Haven Hospital New Haven, CT | B | $11,473 | paid | UNRESTRICTED GENERAL | 202001149349101200 |
| 2018 | Yale New Haven Hospital New Haven, CT | B | $13,248 | paid | UNRESTRICTED GENERAL | 201931149349100133 |
Match tier: A Reported EIN · B Exact name and place · C Strong name match · D Probable name match · U Unresolved. Tiers C and D are inferred, not reported; see how matching works.
Recipient matching for this dataset version has not yet completed its independent precision check. Tier A rows carry the EIN the filer reported; tiers B–D are the matcher's inference and should be read as leads until the check is published on the methodology page.
Source filings
Every figure above is traceable to one of these IRS e-file returns by its OBJECT_ID.
| Form | Tax year | Period end | Filed | Schema | IRS OBJECT_ID |
|---|---|---|---|---|---|
| 990PF | 2025 | 2025-12-31 | not stated | 2025v4.0 | 202621119349101742 |
| 990PF | 2024 | 2024-12-31 | not stated | 2024v5.0 | 202540799349100549 |
| 990PF | 2023 | 2023-12-31 | not stated | 2023v5.0 | 202431079349102208 |
| 990PF | 2022 | 2022-12-31 | not stated | 2022v5.0 | 202311019349100426 |
| 990PF | 2021 | 2021-12-31 | not stated | 2021v4.1 | 202240819349100829 |
| 990PF | 2020 | 2020-12-31 | not stated | 2020v4.0 | 202121059349101602 |
| 990PF | 2019 | 2019-12-31 | not stated | 2019v5.0 | 202001149349101200 |
| 990PF | 2018 | 2018-12-31 | not stated | 2018v3.0 | 201931149349100133 |
Derived from IRS Form 990-PF e-file XML; recipient identities from the IRS Exempt Organizations Business Master File. Dataset version 2026.09.0, built 2026-09-03. The same rows as Parquet: manifest; as JSON: /api/funders/066334735.json.
The same organization elsewhere in the program: exempt status and filing health · federal awards · grant guidance · open opportunities.
This is informational only, derived from public data on the dates shown. It is not an eligibility determination, and not legal, tax, or accounting advice. Verify against the official source before relying on it.