Triple
T15424334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scooby-Doo (2002 film) |
E369466
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | Kent Beyda |
E369466
|
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: Kent Beyda | Statement: [Scooby-Doo (2002 film), editor, Kent Beyda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kent Beyda Context triple: [Scooby-Doo (2002 film), editor, Kent Beyda]
-
A.
Kent Beyda
chosen
Kent Beyda is a film editor best known for his work on major studio comedies and family films, including the 2002 live-action Scooby-Doo movie.
-
B.
Michael Begler
Michael Begler is an American television writer and producer best known for co-creating the period medical drama series "The Knick."
-
C.
Eric Wetzels
Eric Wetzels is a Dutch politician who serves as the chairperson of the People's Party for Freedom and Democracy (VVD).
-
D.
Anthony Meyer
Anthony Meyer is a British politician and former Conservative Member of Parliament best known for mounting a symbolic leadership challenge against Prime Minister Margaret Thatcher in 1989.
-
E.
Kevin Biegel
Kevin Biegel is an American television writer and producer best known for co-creating the sitcom Cougar Town and working on shows like Scrubs and Enlisted.
- 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_69d85a1849f48190bf898068b2806fae |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ec032548190840b558dde6057c7 |
completed | April 16, 2026, 1:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004f372f7c8190ba04b8bd13bff95c |
completed | May 10, 2026, 9:26 a.m. |
Created at: April 10, 2026, 3:20 a.m.