{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:K5BAI7WXSDVGGYU7VI6DFAG7YE","short_pith_number":"pith:K5BAI7WX","canonical_record":{"source":{"id":"2606.01210","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2026-05-31T13:00:57Z","cross_cats_sorted":[],"title_canon_sha256":"dae511172f583b1f590ab25651dfa3a42fe3a9961989b7748f047312431b186a","abstract_canon_sha256":"1f2e64e6d5b3be93ef24e27ac6c5675da96970ba34c36477209408ffd59889bc"},"schema_version":"1.0"},"canonical_sha256":"5742047ed790ea63629faa3c3280dfc13571183004494e802bc232cadef03d2a","source":{"kind":"arxiv","id":"2606.01210","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.01210","created_at":"2026-06-02T02:04:26Z"},{"alias_kind":"arxiv_version","alias_value":"2606.01210v1","created_at":"2026-06-02T02:04:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.01210","created_at":"2026-06-02T02:04:26Z"},{"alias_kind":"pith_short_12","alias_value":"K5BAI7WXSDVG","created_at":"2026-06-02T02:04:26Z"},{"alias_kind":"pith_short_16","alias_value":"K5BAI7WXSDVGGYU7","created_at":"2026-06-02T02:04:26Z"},{"alias_kind":"pith_short_8","alias_value":"K5BAI7WX","created_at":"2026-06-02T02:04:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:K5BAI7WXSDVGGYU7VI6DFAG7YE","target":"record","payload":{"canonical_record":{"source":{"id":"2606.01210","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2026-05-31T13:00:57Z","cross_cats_sorted":[],"title_canon_sha256":"dae511172f583b1f590ab25651dfa3a42fe3a9961989b7748f047312431b186a","abstract_canon_sha256":"1f2e64e6d5b3be93ef24e27ac6c5675da96970ba34c36477209408ffd59889bc"},"schema_version":"1.0"},"canonical_sha256":"5742047ed790ea63629faa3c3280dfc13571183004494e802bc232cadef03d2a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:26.890096Z","signature_b64":"kdNr8kJm8KZ2fH+tK77ul3Fd+ANSTBCNjnS/wB211lcFCa4UArIQTGvnJnsh3Oi36kb4qVDLdeBSYrClh8xGDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5742047ed790ea63629faa3c3280dfc13571183004494e802bc232cadef03d2a","last_reissued_at":"2026-06-02T02:04:26.889694Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:26.889694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.01210","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-02T02:04:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SKAeJlf3JYlDNiJFxTt/SSKqtdo9d/bSIojcwUBy5tbF10HTS1tBf5Mh8uIZcRpn4PPHrRMOWg7Zjgn4l1IpBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T16:15:25.016301Z"},"content_sha256":"351e163ffc6cdf72996bd6a3bc97cdebc168962cf1d7fb503bcd5b0bab0a94af","schema_version":"1.0","event_id":"sha256:351e163ffc6cdf72996bd6a3bc97cdebc168962cf1d7fb503bcd5b0bab0a94af"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:K5BAI7WXSDVGGYU7VI6DFAG7YE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Can we trust LLM Self-Explanations for Entity Resolution?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Divesh Srivastava, Donatella Firmani, Nick Koudas, Paolo Merialdo, Tommaso Teofili","submitted_at":"2026-05-31T13:00:57Z","abstract_excerpt":"Large Language Models (LLMs) have recently shown strong performance on Entity Resolution (ER). Additionally, akin to their prowess in providing accurate predictions, these models often generate self-explanations alongside their predictions through prompting. While such self-explanations are appealing due to their negligible computational cost, their actual reliability remains largely unexplored.\n  In this paper, we present the first large-scale systematic evaluation of LLM self-explanations for ER, focusing on feature attribution and counterfactual explanations at both the attribute and token "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.01210","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.01210/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-02T02:04:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p6bAsGOtFKY1sm1fHOu6tvHlj0IYvp4GM3m2qLmN5XI2C/9visM2mIZgiNdQgs+myHiPK5pzh+pQ3bI85oeUBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T16:15:25.017119Z"},"content_sha256":"03af3c7dc62e966e62826eaa060bd013918df12bfda9c916878682387d84d62b","schema_version":"1.0","event_id":"sha256:03af3c7dc62e966e62826eaa060bd013918df12bfda9c916878682387d84d62b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/K5BAI7WXSDVGGYU7VI6DFAG7YE/bundle.json","state_url":"https://pith.science/pith/K5BAI7WXSDVGGYU7VI6DFAG7YE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/K5BAI7WXSDVGGYU7VI6DFAG7YE/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-08T16:15:25Z","links":{"resolver":"https://pith.science/pith/K5BAI7WXSDVGGYU7VI6DFAG7YE","bundle":"https://pith.science/pith/K5BAI7WXSDVGGYU7VI6DFAG7YE/bundle.json","state":"https://pith.science/pith/K5BAI7WXSDVGGYU7VI6DFAG7YE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/K5BAI7WXSDVGGYU7VI6DFAG7YE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:K5BAI7WXSDVGGYU7VI6DFAG7YE","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1f2e64e6d5b3be93ef24e27ac6c5675da96970ba34c36477209408ffd59889bc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2026-05-31T13:00:57Z","title_canon_sha256":"dae511172f583b1f590ab25651dfa3a42fe3a9961989b7748f047312431b186a"},"schema_version":"1.0","source":{"id":"2606.01210","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.01210","created_at":"2026-06-02T02:04:26Z"},{"alias_kind":"arxiv_version","alias_value":"2606.01210v1","created_at":"2026-06-02T02:04:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.01210","created_at":"2026-06-02T02:04:26Z"},{"alias_kind":"pith_short_12","alias_value":"K5BAI7WXSDVG","created_at":"2026-06-02T02:04:26Z"},{"alias_kind":"pith_short_16","alias_value":"K5BAI7WXSDVGGYU7","created_at":"2026-06-02T02:04:26Z"},{"alias_kind":"pith_short_8","alias_value":"K5BAI7WX","created_at":"2026-06-02T02:04:26Z"}],"graph_snapshots":[{"event_id":"sha256:03af3c7dc62e966e62826eaa060bd013918df12bfda9c916878682387d84d62b","target":"graph","created_at":"2026-06-02T02:04:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.01210/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) have recently shown strong performance on Entity Resolution (ER). Additionally, akin to their prowess in providing accurate predictions, these models often generate self-explanations alongside their predictions through prompting. While such self-explanations are appealing due to their negligible computational cost, their actual reliability remains largely unexplored.\n  In this paper, we present the first large-scale systematic evaluation of LLM self-explanations for ER, focusing on feature attribution and counterfactual explanations at both the attribute and token ","authors_text":"Divesh Srivastava, Donatella Firmani, Nick Koudas, Paolo Merialdo, Tommaso Teofili","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2026-05-31T13:00:57Z","title":"Can we trust LLM Self-Explanations for Entity Resolution?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.01210","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:351e163ffc6cdf72996bd6a3bc97cdebc168962cf1d7fb503bcd5b0bab0a94af","target":"record","created_at":"2026-06-02T02:04:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1f2e64e6d5b3be93ef24e27ac6c5675da96970ba34c36477209408ffd59889bc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2026-05-31T13:00:57Z","title_canon_sha256":"dae511172f583b1f590ab25651dfa3a42fe3a9961989b7748f047312431b186a"},"schema_version":"1.0","source":{"id":"2606.01210","kind":"arxiv","version":1}},"canonical_sha256":"5742047ed790ea63629faa3c3280dfc13571183004494e802bc232cadef03d2a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5742047ed790ea63629faa3c3280dfc13571183004494e802bc232cadef03d2a","first_computed_at":"2026-06-02T02:04:26.889694Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T02:04:26.889694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kdNr8kJm8KZ2fH+tK77ul3Fd+ANSTBCNjnS/wB211lcFCa4UArIQTGvnJnsh3Oi36kb4qVDLdeBSYrClh8xGDQ==","signature_status":"signed_v1","signed_at":"2026-06-02T02:04:26.890096Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.01210","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:351e163ffc6cdf72996bd6a3bc97cdebc168962cf1d7fb503bcd5b0bab0a94af","sha256:03af3c7dc62e966e62826eaa060bd013918df12bfda9c916878682387d84d62b"],"state_sha256":"c218e574a645042c77a1b0b85f845351419dbbe3a65bb0aa704ca89f295efce9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OmdFKtqmLDIP3ydlFqHUX+YVQl1wg9+6LA9HWYICSpNErqaN1BgUkPdQDPODPdVD1UBOl6vAMiNczvGuk20aBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T16:15:25.021598Z","bundle_sha256":"c6b1bf44a29e7f41daa5fe712d49ba1d82d4b5d5f829eb9122257d2bcf7368b1"}}