Triple
T20816891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Copycat |
E512463
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Mark Tarlov |
—
|
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: Mark Tarlov | Statement: [Copycat, producer, Mark Tarlov]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Tarlov Context triple: [Copycat, producer, Mark Tarlov]
-
A.
Mark Tarlov
chosen
Mark Tarlov was an American film producer, director, and winemaker known for producing movies such as "Copycat" and later founding acclaimed Oregon wineries.
-
B.
Mark Korven
Mark Korven is a Canadian film and television composer best known for his unsettling, atmospheric scores for horror projects such as The Witch and The Lighthouse.
-
C.
Matthew Shafer
Matthew Shafer is an American writer known for his work on the animated series "Cowboy Bebop" and related projects.
-
D.
Matthew Shafer
Matthew Shafer, better known by his stage name Uncle Kracker, is an American singer-songwriter and musician recognized for his blend of rock, country, and pop influences.
-
E.
Gary Tarpinian
Gary Tarpinian was an American television producer best known for creating and producing popular nonfiction and reality series, particularly in the history and science genres.
- 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_69e0b4cd25088190b48ca9700cd24efc |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2f3473c81908c43a2ec242b1acd |
completed | April 21, 2026, 12:21 a.m. |
Created at: April 16, 2026, 12:41 p.m.