Triple
T19532701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ATL (2006 film) |
E488693
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Jody Gerson |
—
|
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: Jody Gerson | Statement: [ATL (2006 film), producer, Jody Gerson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jody Gerson Context triple: [ATL (2006 film), producer, Jody Gerson]
-
A.
Jody Gerson
chosen
Jody Gerson is a prominent American music executive and producer, best known as the CEO and Chairman of Universal Music Publishing Group.
-
B.
Jody Landau
Jody Landau is known primarily as the child of acclaimed American film producer Jon Landau, who worked closely with director James Cameron on major blockbuster films.
-
C.
Jody Silverman
Jody Silverman is a character portrayed by actor Zach McGowan.
-
D.
Jody Shapiro
Jody Shapiro is a Canadian film producer and director known for his collaborations with filmmakers like Guy Maddin and Isabella Rossellini on distinctive independent and art-house projects.
-
E.
Jo Eisinger
Jo Eisinger was an American screenwriter best known for his dark, psychologically complex film noir scripts, including classics like "Gilda" and "Night and the City."
- 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_69d8e8db5b6c8190984b61f91981f575 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6364091f4819088b27d0ffdf6010d |
completed | April 20, 2026, 2:20 p.m. |
Created at: April 10, 2026, 1:41 p.m.