Pith Number
pith:4JRUGZDW
pith:2024:4JRUGZDWAXGQ4KW6MLBSH3FQ7I
not attested
not anchored
not stored
refs pending
Are LLMs Good Annotators for Discourse-level Event Relation Extraction?
arxiv:2407.19568 v3 · 2024-07-28 · cs.CL · cs.AI
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{4JRUGZDWAXGQ4KW6MLBSH3FQ7I}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
· sign in to
claim
4
Citations
5
Replications
✓
Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Receipt and verification
| First computed | 2026-07-05T10:18:09.441473Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
e26343647605cd0e2ade62c323ecb0fa3f9776f8a1569568ec51eb8c2fc84928
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/4JRUGZDWAXGQ4KW6MLBSH3FQ7I \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e26343647605cd0e2ade62c323ecb0fa3f9776f8a1569568ec51eb8c2fc84928
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "6164ccb5ab8aea215c63908bd1e414ebd38087c2b75411f692e70c7ab1da5bbe",
"cross_cats_sorted": [
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
"primary_cat": "cs.CL",
"submitted_at": "2024-07-28T19:27:06Z",
"title_canon_sha256": "62211120bfbb901ccddf74f4a064cc01e38234c7744b18e745ce9986565c1e0e"
},
"schema_version": "1.0",
"source": {
"id": "2407.19568",
"kind": "arxiv",
"version": 3
}
}