Triple

T2959087
Position Surface form Disambiguated ID Type / Status
Subject Kaseya Center E80001 entity
Predicate namingRightsPartner P3318 FINISHED
Object Kaseya Limited E313407 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Kaseya Limited | Statement: [Kaseya Center, namingRightsPartner, Kaseya Limited]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kaseya Limited
Context triple: [Kaseya Center, namingRightsPartner, Kaseya Limited]
  • A. Kaseya Limited chosen
    Kaseya Limited is an IT management and security software company known for providing remote monitoring, endpoint management, and cybersecurity solutions to managed service providers and enterprises.
  • B. Ultimate Software
    Ultimate Software was a leading American provider of cloud-based human capital management and payroll software solutions for businesses.
  • C. Syntrillium Software
    Syntrillium Software was a software company best known for creating the audio editing program Cool Edit, which later evolved into Adobe Audition after Adobe acquired the firm.
  • D. Trend Micro
    Trend Micro is a global cybersecurity company known for its antivirus, cloud security, and enterprise threat protection solutions.
  • E. Wyatt Software
    Wyatt Software was a software company known for employing pioneering programmer Ward Cunningham early in his career.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ad8b1341848190bd19dbf46892887d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad992c4c7c819084b5bef299255181 completed March 8, 2026, 3:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69b108de7e3c81908b1fee310515e4c1 completed March 11, 2026, 6:17 a.m.
Created at: March 8, 2026, 2:57 p.m.