Triple
T20620839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Sea of Trees |
E506692
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Gil Netter |
—
|
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: Gil Netter | Statement: [The Sea of Trees, producer, Gil Netter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gil Netter Context triple: [The Sea of Trees, producer, Gil Netter]
-
A.
Gil Netter
chosen
Gil Netter is an American film producer known for backing acclaimed adaptations and dramas such as Life of Pi, The Blind Side, and Water for Elephants.
-
B.
Adam Deitch
Adam Deitch is an American drummer, producer, and songwriter best known for his work in funk, jazz, and hip-hop, including as a core member of the band Lettuce.
-
C.
Michael Burger
Michael Burger is an American television game show host and comedian best known for hosting various revivals and syndicated game shows in the 1990s.
-
D.
Michael Burger
Michael Burger is a legal scholar and environmental law expert known for his leadership in advancing climate change law and policy.
-
E.
Glen Keane
Glen Keane is an acclaimed American animator, author, and illustrator best known for his character animation work on classic Disney films such as "The Little Mermaid," "Beauty and the Beast," and "Aladdin."
- 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_69e0b4bc90988190ac360aaf645efc1d |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6abe19ca481908c896bec49a025cd |
completed | April 20, 2026, 10:42 p.m. |
Created at: April 16, 2026, 11:42 a.m.