Paper Citation Record · LEDGER

Entity Matching using Large Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2310.11244.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2310.11244 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:54:55.859452Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T23:04:10.809698Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5185bc84-7388-4b16-9504-fc4afd2f54b4 · inbound

TransClean: Finding False Positives in Multi-Source Entity Matching under Real-World Conditions via Transitive Consistency cites this paper.

TransClean: Finding False Positives in Multi-Source Entity Matching under Real-World Conditions via Transitive Consistency Entity Matching using Large Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:55.859452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6c0b008-aff3-4ad4-915c-f781d8128709 · inbound

Beyond Traditional Algorithms: Leveraging LLMs for Accurate Cross-Border Entity Identification cites this paper.

Beyond Traditional Algorithms: Leveraging LLMs for Accurate Cross-Border Entity Identification Entity Matching using Large Language Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:32.854374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fc7f2d1-8055-47c8-a2f9-cc3b952511ee · inbound

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution cites this paper.

Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution Entity Matching using Large Language Models

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-05T23:04:11.029413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f344efc-6d15-42f1-9670-7eb5f34c3845 · inbound

Attribution Markets: A Fisher-Market Formulation for Fractional Credit Assignment Between Planned Tasks and Performed Actions cites this paper.

Attribution Markets: A Fisher-Market Formulation for Fractional Credit Assignment Between Planned Tasks and Performed Actions Entity Matching using Large Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-01T09:44:32.064630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.