{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:AQQTFHKAPR7ELCBVAU45CX5ROI","short_pith_number":"pith:AQQTFHKA","canonical_record":{"source":{"id":"2412.20414","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-29T09:47:14Z","cross_cats_sorted":["cs.AI","cs.IR"],"title_canon_sha256":"f7006a7550ce6bdd4a4a3b35351e4ec69e445aca459f56e85978c89743f0e85d","abstract_canon_sha256":"d7f7131d724ab62e96b7aca8135d41b57b537d1b852d3c05717aa82ba9abf35c"},"schema_version":"1.0"},"canonical_sha256":"0421329d407c7e4588350539d15fb17222c91ff715e5bd79924d19ba2ad22e55","source":{"kind":"arxiv","id":"2412.20414","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.20414","created_at":"2026-07-05T09:54:58Z"},{"alias_kind":"arxiv_version","alias_value":"2412.20414v1","created_at":"2026-07-05T09:54:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.20414","created_at":"2026-07-05T09:54:58Z"},{"alias_kind":"pith_short_12","alias_value":"AQQTFHKAPR7E","created_at":"2026-07-05T09:54:58Z"},{"alias_kind":"pith_short_16","alias_value":"AQQTFHKAPR7ELCBV","created_at":"2026-07-05T09:54:58Z"},{"alias_kind":"pith_short_8","alias_value":"AQQTFHKA","created_at":"2026-07-05T09:54:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:AQQTFHKAPR7ELCBVAU45CX5ROI","target":"record","payload":{"canonical_record":{"source":{"id":"2412.20414","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-29T09:47:14Z","cross_cats_sorted":["cs.AI","cs.IR"],"title_canon_sha256":"f7006a7550ce6bdd4a4a3b35351e4ec69e445aca459f56e85978c89743f0e85d","abstract_canon_sha256":"d7f7131d724ab62e96b7aca8135d41b57b537d1b852d3c05717aa82ba9abf35c"},"schema_version":"1.0"},"canonical_sha256":"0421329d407c7e4588350539d15fb17222c91ff715e5bd79924d19ba2ad22e55","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:58.882708Z","signature_b64":"CL68P0AtxaT0nYhCDj60BzWNGBVU8JxaMo5vKaNDPwu81BisR8Uvfs3H0F0bxzNecXI3Lfioi1ODZuv9uriEAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0421329d407c7e4588350539d15fb17222c91ff715e5bd79924d19ba2ad22e55","last_reissued_at":"2026-07-05T09:54:58.882266Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:58.882266Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.20414","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":"2026-07-05T09:54:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qzQs2J8oDyOA5/DlPYpoJdEqPzZ3UVQNTWe9T+Wk1UKo+7mGVh1LnXd4d7qPipKznnmKlWTMV7HdigC/ofZ4Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T22:51:17.455722Z"},"content_sha256":"b18db57d13ab67be45534c3c75195a1dc30369105d851b9644af31f4b9b2a285","schema_version":"1.0","event_id":"sha256:b18db57d13ab67be45534c3c75195a1dc30369105d851b9644af31f4b9b2a285"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:AQQTFHKAPR7ELCBVAU45CX5ROI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Comparative Performance of Advanced NLP Models and LLMs in Multilingual Geo-Entity Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR"],"primary_cat":"cs.CL","authors_text":"Kalin Kopanov","submitted_at":"2024-12-29T09:47:14Z","abstract_excerpt":"The integration of advanced Natural Language Processing (NLP) methodologies and Large Language Models (LLMs) has significantly enhanced the extraction and analysis of geospatial data from multilingual texts, impacting sectors such as national and international security. This paper presents a comprehensive evaluation of leading NLP models -- SpaCy, XLM-RoBERTa, mLUKE, GeoLM -- and LLMs, specifically OpenAI's GPT 3.5 and GPT 4, within the context of multilingual geo-entity detection. Utilizing datasets from Telegram channels in English, Russian, and Arabic, we examine the performance of these mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.20414","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/2412.20414/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":"2026-07-05T09:54:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bnAZybeoqB0C1K0OigeXTZCMF8YcypUpd3QE/2ohXf7mjAvLBFS7sNGh32G3qN0rJzKxO4cQoU1jRC2JKRNSAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T22:51:17.456275Z"},"content_sha256":"54b1ce22ec7383e560a1b5016712838d42c8c4e28ef9ad14924b34506a7bed60","schema_version":"1.0","event_id":"sha256:54b1ce22ec7383e560a1b5016712838d42c8c4e28ef9ad14924b34506a7bed60"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AQQTFHKAPR7ELCBVAU45CX5ROI/bundle.json","state_url":"https://pith.science/pith/AQQTFHKAPR7ELCBVAU45CX5ROI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AQQTFHKAPR7ELCBVAU45CX5ROI/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":"2026-08-14T22:51:17Z","links":{"resolver":"https://pith.science/pith/AQQTFHKAPR7ELCBVAU45CX5ROI","bundle":"https://pith.science/pith/AQQTFHKAPR7ELCBVAU45CX5ROI/bundle.json","state":"https://pith.science/pith/AQQTFHKAPR7ELCBVAU45CX5ROI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AQQTFHKAPR7ELCBVAU45CX5ROI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:AQQTFHKAPR7ELCBVAU45CX5ROI","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":"d7f7131d724ab62e96b7aca8135d41b57b537d1b852d3c05717aa82ba9abf35c","cross_cats_sorted":["cs.AI","cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-29T09:47:14Z","title_canon_sha256":"f7006a7550ce6bdd4a4a3b35351e4ec69e445aca459f56e85978c89743f0e85d"},"schema_version":"1.0","source":{"id":"2412.20414","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.20414","created_at":"2026-07-05T09:54:58Z"},{"alias_kind":"arxiv_version","alias_value":"2412.20414v1","created_at":"2026-07-05T09:54:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.20414","created_at":"2026-07-05T09:54:58Z"},{"alias_kind":"pith_short_12","alias_value":"AQQTFHKAPR7E","created_at":"2026-07-05T09:54:58Z"},{"alias_kind":"pith_short_16","alias_value":"AQQTFHKAPR7ELCBV","created_at":"2026-07-05T09:54:58Z"},{"alias_kind":"pith_short_8","alias_value":"AQQTFHKA","created_at":"2026-07-05T09:54:58Z"}],"graph_snapshots":[{"event_id":"sha256:54b1ce22ec7383e560a1b5016712838d42c8c4e28ef9ad14924b34506a7bed60","target":"graph","created_at":"2026-07-05T09:54:58Z","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/2412.20414/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The integration of advanced Natural Language Processing (NLP) methodologies and Large Language Models (LLMs) has significantly enhanced the extraction and analysis of geospatial data from multilingual texts, impacting sectors such as national and international security. This paper presents a comprehensive evaluation of leading NLP models -- SpaCy, XLM-RoBERTa, mLUKE, GeoLM -- and LLMs, specifically OpenAI's GPT 3.5 and GPT 4, within the context of multilingual geo-entity detection. Utilizing datasets from Telegram channels in English, Russian, and Arabic, we examine the performance of these mo","authors_text":"Kalin Kopanov","cross_cats":["cs.AI","cs.IR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-29T09:47:14Z","title":"Comparative Performance of Advanced NLP Models and LLMs in Multilingual Geo-Entity Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.20414","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:b18db57d13ab67be45534c3c75195a1dc30369105d851b9644af31f4b9b2a285","target":"record","created_at":"2026-07-05T09:54:58Z","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":"d7f7131d724ab62e96b7aca8135d41b57b537d1b852d3c05717aa82ba9abf35c","cross_cats_sorted":["cs.AI","cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-29T09:47:14Z","title_canon_sha256":"f7006a7550ce6bdd4a4a3b35351e4ec69e445aca459f56e85978c89743f0e85d"},"schema_version":"1.0","source":{"id":"2412.20414","kind":"arxiv","version":1}},"canonical_sha256":"0421329d407c7e4588350539d15fb17222c91ff715e5bd79924d19ba2ad22e55","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0421329d407c7e4588350539d15fb17222c91ff715e5bd79924d19ba2ad22e55","first_computed_at":"2026-07-05T09:54:58.882266Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:54:58.882266Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CL68P0AtxaT0nYhCDj60BzWNGBVU8JxaMo5vKaNDPwu81BisR8Uvfs3H0F0bxzNecXI3Lfioi1ODZuv9uriEAg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:54:58.882708Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.20414","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b18db57d13ab67be45534c3c75195a1dc30369105d851b9644af31f4b9b2a285","sha256:54b1ce22ec7383e560a1b5016712838d42c8c4e28ef9ad14924b34506a7bed60"],"state_sha256":"32d77cacd451331007cc86ab2ba4715004d207a424ccc5eb28002508253a3659"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Rp4ube+JXeMClIDsAto2JrV16bgq6SOc/3GZSbOH3m2r/uhauwNi8E8nIes7Jk952TguANEH5cdvlTsOjZ+VDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T22:51:17.459776Z","bundle_sha256":"e2bd7bed2aca128035bdbd1b9425e5f1b191aa0685967465ab35906070eedfd1"}}