{"as_of":"2026-08-07T22:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f78852862d0bc25394e40dcd722e6306b54040f476cd821cfadb256e1619fb66","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T14:12:30.936128Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.19710/citation-record","integrity":"/paper/2507.19710/integrity","json":"/paper/2507.19710/citation-record.json","paper":"/paper/2507.19710"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:12:33.807541Z","title":null,"venue":null,"work_id":"aee51a68-89f6-4b1b-bf58-20ade648235a","year":2024},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:28.794004Z"},"links":{"citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:e11a7a5db2e556a50e559f44cd47e0cc9ae51e97c4771bd518491907c628d093","observation_id":"c5f301be-2204-4afe-8cdf-3b056e343aa0","resolution":{"observed_at":"2026-08-06T14:12:33.922297Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:12:33.619379Z","title":null,"venue":null,"work_id":"08c30db7-58f5-426c-aae9-eda46e384c18","year":2007},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:28.828238Z"},"links":{"citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:7e819edde056ba7a3e90aa92ad6d5f366b8b5041c44d8a4974548a4827efc21a","observation_id":"dadeb51f-22ad-4cbc-91f1-ef032e664bd9","resolution":{"observed_at":"2026-08-06T14:12:33.677650Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-06T14:12:28.914253Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:28.914253Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:e8164e10082a4a0c3e3989dd11b04a55839977da76a35b38cd2c459495672f85","observation_id":"4d959341-037b-4c17-95ab-525812f51c24","resolution":{"observed_at":"2026-08-06T14:12:28.914253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.10404","last_updated":"2020-04-28T00:26:21Z","snapshot_observed_at":"2026-08-07T17:15:36.901925Z","submitted_at":"2020-04-22T06:03:10Z","title":"Logical Natural Language Generation from Open-Domain Tables","version":2},"cited_work":{"arxiv_id":"2004.10404","doi":null,"metadata_source":"pith","pith_arxiv_id":"2004.10404","snapshot_observed_at":"2026-08-06T14:12:32.591228Z","title":"Logical Natural Language Generation from Open-Domain Tables","venue":"cs.CL","work_id":"2e2fe64e-6b40-4c1c-938e-3208d8f83ff2","year":2020},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:28.996508Z"},"links":{"cited_paper":"/paper/2004.10404","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:4b36986179c62cf594f310872aa067ba2d079ba8d168add3896bafccbdfcc46e","observation_id":"72b9b1ef-b515-4090-811d-94a25d111478","resolution":{"observed_at":"2026-08-06T14:12:32.654565Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:12:29.050308Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:29.050308Z"},"links":{"citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:3d15ebd22d2ad9df3e17d8a1a2432053ed052f4d5b71fa43db3db8aacdf2c6f6","observation_id":"9d3765b1-5df2-450d-8663-a61d3a1cf834","resolution":{"observed_at":"2026-08-06T14:12:29.050308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10560","last_updated":"2024-06-15T08:41:44Z","snapshot_observed_at":"2026-08-03T14:28:05.592182Z","submitted_at":"2024-06-15T08:41:44Z","title":"Facts-and-Feelings: Capturing both Objectivity and Subjectivity in Table-to-Text Generation","version":1},"cited_work":{"arxiv_id":"2406.10560","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.10560","snapshot_observed_at":"2026-08-06T14:12:32.400616Z","title":"Facts-and-Feelings: Capturing both Objectivity and Subjectivity in Table-to-Text Generation","venue":"cs.CL","work_id":"bdba166e-14d1-4f57-82b5-fb13d897e14b","year":2024},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:29.141731Z"},"links":{"cited_paper":"/paper/2406.10560","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:38b619386c22160001c5d1246062819e281be11f119d9fbdb162d15f40b07e7a","observation_id":"ece5311f-2eff-4c28-a50c-3efabda5227a","resolution":{"observed_at":"2026-08-06T14:12:32.486808Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.09022","last_updated":"2019-11-27T13:06:04Z","snapshot_observed_at":"2026-08-07T13:20:30.972282Z","submitted_at":"2019-08-23T20:10:36Z","title":"Neural data-to-text generation: A comparison between pipeline and end-to-end architectures","version":2},"cited_work":{"arxiv_id":"1908.09022","doi":null,"metadata_source":"pith","pith_arxiv_id":"1908.09022","snapshot_observed_at":"2026-08-06T14:12:32.252592Z","title":"Neural data-to-text generation: A comparison between pipeline and end-to-end architectures","venue":"cs.CL","work_id":"95a2e6ca-574f-49c3-b197-e70fd608e81b","year":2019},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:29.209288Z"},"links":{"cited_paper":"/paper/1908.09022","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:b9bf06d66c65b9b47761eed1404473e23591142c91b2d9a2c3bfd6eabcb2991d","observation_id":"0690ac02-9da8-43bf-bc1c-90fca6fab256","resolution":{"observed_at":"2026-08-06T14:12:32.309248Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:12:29.292360Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:29.292360Z"},"links":{"citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:5555a1482f1c1c672b2db22d18ac48f52a8a3a6973484d5f9d09eec4af64c426","observation_id":"dac88c6f-14ba-4b47-bb59-bff297c94b9a","resolution":{"observed_at":"2026-08-06T14:12:29.292360Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:12:33.436366Z","title":null,"venue":null,"work_id":"197af5c5-f287-4bd9-9117-fd7e1068e6b4","year":2018},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:29.340804Z"},"links":{"citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:937a7e34dfffd1562a213e0777dc03c405c01829217ec466dfd9d93ed2d8a071","observation_id":"1ea4fefe-f513-401b-9c77-8b34b873c990","resolution":{"observed_at":"2026-08-06T14:12:33.543942Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.14497","last_updated":"2025-02-14T12:38:15Z","snapshot_observed_at":"2026-07-06T20:25:36.218904Z","submitted_at":"2025-01-24T13:53:54Z","title":"Evaluating and Improving Graph to Text Generation with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.14497","snapshot_observed_at":"2026-08-06T14:12:29.435039Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:29.435039Z"},"links":{"cited_paper":"/paper/2501.14497","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:1c7ad54fb753a147d57254eaac8d672ef28d8d0c9dd080d4e574a7b9f0da1e46","observation_id":"79cc1587-5475-4820-b43b-00e479979c81","resolution":{"observed_at":"2026-08-06T14:12:29.435039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.02349","last_updated":"2020-04-21T15:09:48Z","snapshot_observed_at":"2026-08-04T23:31:44.274839Z","submitted_at":"2020-04-05T23:18:37Z","title":"TAPAS: Weakly Supervised Table Parsing via Pre-training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.02349","snapshot_observed_at":"2026-08-06T14:12:29.491694Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:29.491694Z"},"links":{"cited_paper":"/paper/2004.02349","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:037d71e45cdc4a5ca01993233ff0da59db5abf2e36529834a0013433de6e4ca2","observation_id":"d4455b02-96e5-4c6b-8e77-c50eef707bef","resolution":{"observed_at":"2026-08-06T14:12:29.491694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06825","last_updated":"2023-10-10T17:54:58Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-10-10T17:54:58Z","title":"Mistral 7B","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06825","snapshot_observed_at":"2026-08-06T14:12:29.574839Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:29.574839Z"},"links":{"cited_paper":"/paper/2310.06825","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:06dd2f65d49c99a92e946966788d43021ff4a112839d6a9453a93eae19febea7","observation_id":"81852f41-b53a-4028-8dfb-a82846a62646","resolution":{"observed_at":"2026-08-06T14:12:29.574839Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.18672","last_updated":"2024-12-24T20:16:10Z","snapshot_observed_at":"2026-07-06T20:12:59.152345Z","submitted_at":"2024-12-24T20:16:10Z","title":"From Hallucinations to Facts: Enhancing Language Models with Curated Knowledge Graphs","version":1},"cited_work":{"arxiv_id":"2412.18672","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.18672","snapshot_observed_at":"2026-08-06T14:12:32.024889Z","title":"From Hallucinations to Facts: Enhancing Language Models with Curated Knowledge Graphs","venue":"cs.CL","work_id":"3b50102c-d748-451c-ae7e-2d3dccab6516","year":2024},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:29.628663Z"},"links":{"cited_paper":"/paper/2412.18672","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:8ee0c96610aaff2fa812bff9003f4acec58dd4c96daca45e0e35215b037afa9a","observation_id":"9917db50-c888-4acc-9b77-2211aadf4a93","resolution":{"observed_at":"2026-08-06T14:12:32.124306Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.10433","last_updated":"2021-07-09T00:42:32Z","snapshot_observed_at":"2026-08-05T08:38:03.497919Z","submitted_at":"2020-05-21T02:46:15Z","title":"Text-to-Text Pre-Training for Data-to-Text Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.10433","snapshot_observed_at":"2026-08-06T14:12:29.680788Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:29.680788Z"},"links":{"cited_paper":"/paper/2005.10433","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:3456937ae20cb15380b7f73e973b0440965977dc6fa2fe98310445ee867db314","observation_id":"70bc8335-01b5-4796-a38a-b642b5c76771","resolution":{"observed_at":"2026-08-06T14:12:29.680788Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:12:33.266689Z","title":null,"venue":null,"work_id":"2c2911dd-c7c9-4371-98fb-2c02e2e9d676","year":2020},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:29.744534Z"},"links":{"citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:88321d53bf1167bd442dd9eb571df37adf250b7d6eb7fa539c125493de3f6701","observation_id":"766ccc64-374c-4766-8a8f-25d8037926ac","resolution":{"observed_at":"2026-08-06T14:12:33.340417Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2022.emnlp-main.373","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:12:31.061238Z","title":null,"venue":null,"work_id":"0b98e935-727c-4e71-83ae-c379525f5e39","year":2022},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:29.785709Z"},"links":{"citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:19f43442e508a1428c13a2f0137a5278c23df4de5810d28476217b7511c73061","observation_id":"500ef007-d4bc-4bae-ac05-f70cfedfda41","resolution":{"observed_at":"2026-08-06T14:12:31.115256Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"cs/0409058","last_updated":"2004-09-29T20:34:04Z","snapshot_observed_at":"2026-07-07T03:06:00.568661Z","submitted_at":"2004-09-29T20:34:04Z","title":"A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"cs/0409058","snapshot_observed_at":"2026-08-06T14:12:29.862077Z","title":null,"venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:29.862077Z"},"links":{"cited_paper":"/paper/cs/0409058","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:0336cadb0eb97f1e9b1f14b921c1b1c835a7c9f3ea0f3838ce3718f0d6becf1c","observation_id":"c2a3eda4-caef-4196-9feb-772dc874b246","resolution":{"observed_at":"2026-08-06T14:12:29.862077Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:12:29.945880Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:29.945880Z"},"links":{"citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:85c3d855a8d75d66e83f38048a6ce7d17d54174adc3d934e2ee0f5fa0c5bc252","observation_id":"e7c6b057-dcab-457c-9595-471e340583a8","resolution":{"observed_at":"2026-08-06T14:12:29.945880Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:12:33.093540Z","title":null,"venue":null,"work_id":"28c6e925-c94a-4315-8bb3-516ebab63a22","year":2020},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:30.010277Z"},"links":{"citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:7365d39d557403bf1d811d07a0da5cd40381ec658bf11a0040d43201201dd0f0","observation_id":"41f4e4bb-69ee-47bd-bb87-9b22c82c4be8","resolution":{"observed_at":"2026-08-06T14:12:33.143927Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:12:30.100446Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:30.100446Z"},"links":{"citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:a2ad74ec29c150d1bab9b6f4ffc6296576d5036325f958bbaaa00b9dabecf5c8","observation_id":"6e927397-1410-4bbb-b8c6-ea424487cac2","resolution":{"observed_at":"2026-08-06T14:12:30.100446Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:12:32.908657Z","title":null,"venue":null,"work_id":"dcd7e76d-15ad-4ee0-afb1-769ee5adaf57","year":2007},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:30.152185Z"},"links":{"citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:192a0f568f6e85085ac3a8cc5954a31f9d5327827f5733b05323cb081c017eeb","observation_id":"f26b00c7-ea66-46ae-88f8-f82f0cda2c80","resolution":{"observed_at":"2026-08-06T14:12:32.982605Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:12:32.788358Z","title":null,"venue":null,"work_id":"f76154d9-d219-40b9-b3f2-b4dc723a7e25","year":1997},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:30.245833Z"},"links":{"citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:8b809f6424028097ac9b0133060ca052c7653ac8c757ee8a3fb1ffd4b2b3f37a","observation_id":"44917221-8c2f-42f0-8213-f70639510905","resolution":{"observed_at":"2026-08-06T14:12:32.863418Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.08426","last_updated":"2021-09-27T13:50:11Z","snapshot_observed_at":"2026-08-05T21:08:42.474132Z","submitted_at":"2020-07-16T16:05:34Z","title":"Investigating Pretrained Language Models for Graph-to-Text Generation","version":3},"cited_work":{"arxiv_id":"2007.08426","doi":null,"metadata_source":"pith","pith_arxiv_id":"2007.08426","snapshot_observed_at":"2026-08-06T14:12:31.804511Z","title":"Investigating Pretrained Language Models for Graph-to-Text Generation","venue":"cs.CL","work_id":"8a67142f-af62-40fc-b680-11c2bb0d407d","year":2020},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:30.298801Z"},"links":{"cited_paper":"/paper/2007.08426","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:910a625282dea4459204810e0b8786b7f20149cf411ccbbadf73c15695e9e0e7","observation_id":"9d82261c-cbfd-4fd5-b468-142400e0ed43","resolution":{"observed_at":"2026-08-06T14:12:31.871126Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.01096","last_updated":"2020-05-03T14:28:28Z","snapshot_observed_at":"2026-07-31T17:54:56.982789Z","submitted_at":"2020-05-03T14:28:28Z","title":"Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence","version":1},"cited_work":{"arxiv_id":"2005.01096","doi":null,"metadata_source":"pith","pith_arxiv_id":"2005.01096","snapshot_observed_at":"2026-08-06T14:12:31.645550Z","title":"Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence","venue":"cs.CL","work_id":"7ad4afa3-3bf8-4444-8f42-8afc7f48bf32","year":2020},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:30.372394Z"},"links":{"cited_paper":"/paper/2005.01096","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:83cf6053060206e11655fac53d8ca975781852f46984c0dde65eeda2a36ffa14","observation_id":"11d64d8d-2307-43ac-abc3-aa3d3df0e26c","resolution":{"observed_at":"2026-08-06T14:12:31.715632Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-06T14:12:30.434790Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:30.434790Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:8c971f834ff68ba79404977072f202e12f52a1445a0e4de95092e04a9b4cf28a","observation_id":"5fb22032-3020-4055-a15c-5fd1b13f5df3","resolution":{"observed_at":"2026-08-06T14:12:30.434790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.08021","last_updated":"2021-05-30T22:47:33Z","snapshot_observed_at":"2026-08-02T00:53:11.096901Z","submitted_at":"2021-05-17T17:15:29Z","title":"Stage-wise Fine-tuning for Graph-to-Text Generation","version":2},"cited_work":{"arxiv_id":"2105.08021","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.08021","snapshot_observed_at":"2026-08-06T14:12:31.455571Z","title":"Stage-wise Fine-tuning for Graph-to-Text Generation","venue":"cs.CL","work_id":"09ae736f-a939-4f4a-9900-661947e815fd","year":2021},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:30.505552Z"},"links":{"cited_paper":"/paper/2105.08021","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:f6fc7abcb4106270400f74ed9976c35c67cd3cfd65b550f88618cda838971344","observation_id":"21e6a4e1-ec9b-4d30-b5a6-187924a105d8","resolution":{"observed_at":"2026-08-06T14:12:31.540710Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.08052","last_updated":"2017-07-25T15:42:25Z","snapshot_observed_at":"2026-08-02T19:51:15.533678Z","submitted_at":"2017-07-25T15:42:25Z","title":"Challenges in Data-to-Document Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.08052","snapshot_observed_at":"2026-08-06T14:12:30.585747Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:30.585747Z"},"links":{"cited_paper":"/paper/1707.08052","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:36f7ff2dee291fb862fd369edb4b5e0dc193a3ffb7fdc221f7477b0568695afd","observation_id":"60a559b0-a1ff-47b3-8686-1fbdc8e0cfe1","resolution":{"observed_at":"2026-08-06T14:12:30.585747Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.08314","last_updated":"2020-05-17T17:26:40Z","snapshot_observed_at":"2026-08-07T05:09:00.943656Z","submitted_at":"2020-05-17T17:26:40Z","title":"TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.08314","snapshot_observed_at":"2026-08-06T14:12:30.646348Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:30.646348Z"},"links":{"cited_paper":"/paper/2005.08314","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:e6c0a784870cf14bbfb09b7492f1466f9370e21daec3b38047c8bc1436cc7ea1","observation_id":"875da0b3-02bb-4d3d-97e0-b3d513bead44","resolution":{"observed_at":"2026-08-06T14:12:30.646348Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.01124","last_updated":"2025-01-02T07:45:34Z","snapshot_observed_at":"2026-07-06T20:15:40.852454Z","submitted_at":"2025-01-02T07:45:34Z","title":"Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.01124","snapshot_observed_at":"2026-08-06T14:12:30.737716Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:30.737716Z"},"links":{"cited_paper":"/paper/2501.01124","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:67eed6344321beebe6124233ebd71ea9f2edd267b44532e1fda447cf325aeb89","observation_id":"03dacd68-5c3f-41ef-a492-2282de1aeca9","resolution":{"observed_at":"2026-08-06T14:12:30.737716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.14712","last_updated":"2023-07-27T09:03:05Z","snapshot_observed_at":"2026-08-05T03:03:43.918059Z","submitted_at":"2023-07-27T09:03:05Z","title":"Evaluating Generative Models for Graph-to-Text Generation","version":1},"cited_work":{"arxiv_id":"2307.14712","doi":null,"metadata_source":"pith","pith_arxiv_id":"2307.14712","snapshot_observed_at":"2026-08-06T14:12:31.237390Z","title":"Evaluating Generative Models for Graph-to-Text Generation","venue":"cs.CL","work_id":"b7a81028-433b-4fcd-a178-cb516ca83fa0","year":2023},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:30.791739Z"},"links":{"cited_paper":"/paper/2307.14712","citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:6cd058aa1eb3f97f658ea23be934c5d35d08d48537ce3473d8377166f5ef0597","observation_id":"c7271297-b5d0-4e30-a462-3da1f78ac0bb","resolution":{"observed_at":"2026-08-06T14:12:31.289680Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:12:30.876174Z","title":"URL: \" 'urlintro :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:30.876174Z"},"links":{"citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:257bfece7a6044cc2e67d0e22c87c13afacd2dd1bc4f496f20348d785c80a948","observation_id":"a35fe25c-c342-4f21-a57b-c543daa8550a","resolution":{"observed_at":"2026-08-06T14:12:30.876174Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:12:30.936128Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-06T14:12:30.936128Z"},"links":{"citing_paper":"/paper/2507.19710"},"observation_digest":"sha256:869c30c2d33958c0e0b102e67a78f33600a4eb853c124542a5af8b7109767c65","observation_id":"500a8d28-36ed-4688-9bfa-288c05b7d43d","resolution":{"observed_at":"2026-08-06T14:12:30.936128Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.19710","last_updated":"2025-07-25T23:06:00Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T03:23:09.554221Z","submitted_at":"2025-07-25T23:06:00Z","title":"Ta-G-T: Subjectivity Capture in Table to Text Generation via RDF Graphs"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":23,"verified_exact":9,"verified_fuzzy":0},"total_outbound_references":32},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2507.19710."}