Triple

T17598756
Position Surface form Disambiguated ID Type / Status
Subject MySQL AB E428638 entity
Predicate notableEmployee P304 FINISHED
Object Monty Widenius NE NERFINISHED

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: Monty Widenius | Statement: [MySQL AB, notableEmployee, Monty Widenius]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Monty Widenius
Context triple: [MySQL AB, notableEmployee, Monty Widenius]
  • A. Michael Widenius chosen
    Michael Widenius is a Finnish software engineer and entrepreneur best known as the original developer of the MySQL relational database and later the founder of MariaDB.
  • B. Maria Widenius
    Maria Widenius is known primarily as the daughter of Finnish software developer Michael "Monty" Widenius, the original creator of the MySQL database.
  • C. David Axmark
    David Axmark is a Swedish software developer best known as one of the original co-founders and developers of the MySQL relational database management system.
  • D. Michael Stonebraker
    Michael Stonebraker is an influential American computer scientist and database pioneer known for creating several landmark database systems and shaping modern data management.
  • E. Jim Gray
    Jim Gray was a pioneering computer scientist renowned for his foundational work in database systems and transaction processing, which earned him numerous top honors in the field.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889e1030481909950e140c63255b9 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e46c474e5481909d2736241b592dab completed April 19, 2026, 5:46 a.m.
Created at: April 10, 2026, 5:51 a.m.