Grants paid by Noah Langholz Remembrance Fund
EIN 81-1775265 · Altadena, CA · Form 990-PF, Part XV · NTEE F12
Noah Langholz Remembrance Fund of Altadena, CA (EIN 81-1775265) reported 12 grants paid totaling $15,000 to 4 recipients in tax years 2023–2025, on Form 990-PF, Part XV. Derived from IRS e-file data, dataset version 2026.09.0, built 2026-09-03.
Grants paid
$15,000
12 grants
Recipients
4
distinct organizations
Tax years
2023–2025
3 filings in the corpus
By tax year
| Tax year | Grants paid | Amount paid | Approved for future |
|---|---|---|---|
| 2023 | 4 | $5,500 | — |
| 2024 | 4 | $5,000 | — |
| 2025 | 4 | $4,500 | — |
Top recipients
The 4 recipients receiving the most from Noah Langholz Remembrance Fund, by grants paid, out of 4 distinct recipients.
| Recipient | Match | Location | Grants | Total paid | Latest year |
|---|---|---|---|---|---|
| Jed Foundation | U | Boston, MA | 3 | $8,500 | 2025 |
| Wesleyan University 58-2039996 | B | Middletown, CT | 3 | $3,000 | 2025 |
| Pasadena Education Foundation 23-7149451 | C | Pasadena, CA | 3 | $2,000 | 2025 |
| Waverly School 95-4869859 | B | Pasadena, CA | 3 | $1,500 | 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 12 grant rows this organization reported, most recent first.
| Tax year | Recipient | Match | Amount | Type | Purpose | Source filing |
|---|---|---|---|---|---|---|
| 2025 | Jed Foundation Boston, MA | U | $2,500 | paid | CHARITABLE | 202601049349101020 |
| 2025 | Wesleyan University Middletown, CT | B | $1,000 | paid | CHARITABLE | 202601049349101020 |
| 2025 | Waverly School Pasadena, CA | B | $500 | paid | CHARITABLE | 202601049349101020 |
| 2025 | Pasadena Education Foundation Pasadena, CA | C | $500 | paid | CHARITABLE | 202601049349101020 |
| 2024 | Jed Foundation Boston, MA | U | $3,000 | paid | CHARITABLE | 202520539349100702 |
| 2024 | Wesleyan University Middletown, CT | B | $1,000 | paid | CHARITABLE | 202520539349100702 |
| 2024 | Waverly School Pasadena, CA | B | $500 | paid | CHARITABLE | 202520539349100702 |
| 2024 | Pasadena Education Foundation Pasadena, CA | C | $500 | paid | CHARITABLE | 202520539349100702 |
| 2023 | Jed Foundation Boston, MA | U | $3,000 | paid | CHARITABLE | 202421029349100822 |
| 2023 | Wesleyan University Middletown, CT | B | $1,000 | paid | CHARITABLE | 202421029349100822 |
| 2023 | Pasadena Education Foundation Pasadena, CA | C | $1,000 | paid | CHARITABLE | 202421029349100822 |
| 2023 | Waverly School Pasadena, CA | B | $500 | paid | CHARITABLE | 202421029349100822 |
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 | 2025v4.1 | 202601049349101020 |
| 990PF | 2024 | 2024-12-31 | not stated | 2024v5.0 | 202520539349100702 |
| 990PF | 2023 | 2023-12-31 | not stated | 2023v5.0 | 202421029349100822 |
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/811775265.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.