Triple
T14430537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Transmeta Corporation |
E357814
|
entity |
| Predicate | founder |
P104
|
FINISHED |
| Object | David Ditzel |
E357814
|
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: David Ditzel | Statement: [Transmeta Corporation, founder, David Ditzel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Ditzel Context triple: [Transmeta Corporation, founder, David Ditzel]
-
A.
David Ditzel
chosen
David Ditzel is a computer engineer and entrepreneur best known as the founder of Transmeta and for his work on low-power, innovative microprocessor designs.
-
B.
John Diehl
John Diehl is an American character actor best known for his role as Detective Larry Zito on the 1980s television series "Miami Vice."
-
C.
Dennis Dreith
Dennis Dreith is an American composer, orchestrator, and music industry executive known for his work on film and television scores and for advocating for musicians’ rights.
-
D.
John Dierkes
John Dierkes was an American character actor known for his tall, gaunt appearance and roles in mid-20th-century Westerns and war films.
-
E.
William Diehl
William Diehl was an American novelist best known for his gritty, suspenseful legal and crime thrillers.
- 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_69d8279402a88190821ffa39ae15bccf |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de914570f08190b1c7c1c57a0cb476 |
completed | April 14, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe7e7563188190b50c4413cd5dde37 |
completed | May 9, 2026, 12:23 a.m. |
Created at: April 10, 2026, 1:18 a.m.