Triple

T15666922
Position Surface form Disambiguated ID Type / Status
Subject Jeffrey P. Buzen E377209 entity
Predicate coFounderOf P104 FINISHED
Object BGS Systems E1170207 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: BGS Systems | Statement: [Jeffrey P. Buzen, coFounderOf, BGS Systems]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: BGS Systems
Context triple: [Jeffrey P. Buzen, coFounderOf, BGS Systems]
  • A. BGS Systems chosen
    BGS Systems was a software company known for developing performance management and capacity planning tools for mainframe and enterprise computer systems.
  • B. Brown & Root
    Brown & Root is a major American engineering and construction company historically known for large-scale industrial, infrastructure, and government projects.
  • C. Sogeti
    Sogeti is a professional services and technology consulting company specializing in IT and engineering solutions, operating as a subsidiary of Capgemini.
  • D. Tokutek
    Tokutek was a software company best known for creating high-performance storage engines for MySQL and MariaDB, particularly the fractal tree–based TokuDB.
  • E. Granite Systems
    Granite Systems was a high-performance network switch startup co-founded by Sun Microsystems co-founder Andy Bechtolsheim and later acquired by Cisco Systems.
  • 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_69d85cd2e28481909d4e975bee20872f completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04f1151548190a14607e762686cb1 completed April 16, 2026, 2:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff6ed8d9188190a68035d2508b117d completed May 9, 2026, 5:28 p.m.
Created at: April 10, 2026, 4:16 a.m.