Triple
T4278943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Widenius |
E97105
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Widenius |
E97105
|
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: Widenius | Statement: [Michael Widenius, familyName, Widenius]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Widenius Context triple: [Michael Widenius, familyName, 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.
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.
-
C.
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.
-
D.
D. Richard Hipp
D. Richard Hipp is an American computer programmer best known as the creator of the SQLite database engine.
-
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 (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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b350201ac88190b9d8980da5f0d03d |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5d069a3c08190abbbf4f163c31054 |
completed | March 14, 2026, 9:17 p.m. |
Created at: March 12, 2026, 11:07 p.m.