Pith. sign in

Paper Citation Record · LEDGER

Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2405.16884.

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

pith.paper-citation-record.v1
2405.16884 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:05:18.965100Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T06:34:18.875283Z

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 0d7abe79-8dd6-409e-9fa4-62d84293245f · inbound

Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An LLM-based Approach cites this paper.

Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An LLM-based Approach Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-08T11:05:18.965100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:05:18.965100Z digest=sha256:ef0c02abb15f303d82473cea62a5a7bb93d4c19356e446b776e97d8faeeb4632

Observation d6cc54d6-2c4b-4b5a-abe0-ad597269352f · 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 Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T10:54:55.900374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:54:55.900374Z digest=sha256:32c8c86a52cd1ed2e0fa3078903abb5a7bbd89cd3c00c4f48e784b230e242506

Observation 3ecc946a-9536-4f22-b85b-402edb3acb8b · 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 Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T17:21:32.916654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:21:32.916654Z digest=sha256:15fe93b9f0cd9957228a200d459184f58cb2534a38e79843431f72aacc2936e2

Observation cb74506c-9176-434f-b3cf-28f1bca48a0e · 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 Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-05T23:04:08.981644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T23:04:08.981644Z digest=sha256:b860a1753fecc5847bfcbc903d7743c84c616be47455e6074309568081a87e65

Observation f4fde809-4ba3-4169-ad8b-75e9745b69fb · inbound

Managing Map Cardinality in Automatic Disease Classification Mapping: Balancing Precision, Recall and Coverage cites this paper.

Managing Map Cardinality in Automatic Disease Classification Mapping: Balancing Precision, Recall and Coverage Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-06-30T06:34:18.877012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-30T06:31:19.781389Z digest=sha256:b0e73ef8ab4cdc265d8a7128bfe72f3318dbee7748ca202e716b3db158d9b788