Triple
T22103879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Education (2019 film) |
E546236
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Fred Berger |
—
|
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: Fred Berger | Statement: [Bad Education (2019 film), producer, Fred Berger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fred Berger Context triple: [Bad Education (2019 film), producer, Fred Berger]
-
A.
Fred Berger
chosen
Fred Berger is a film producer best known for his work on acclaimed movies such as "La La Land" and other high-profile Hollywood projects.
-
B.
Edward Berger
Edward Berger is a German film and television director known for his acclaimed work on series like "Patrick Melrose" and the Oscar-winning war drama "All Quiet on the Western Front."
-
C.
Phil Berger
Phil Berger is an American Republican politician who serves as a powerful leader in the North Carolina State Senate.
-
D.
Fred Bauer
Fred Bauer was a film producer best known for his work on the 1981 fantasy-comedy movie "Under the Rainbow."
-
E.
Glenn Berger
Glenn Berger is an American screenwriter best known for co-writing major animated films such as the Kung Fu Panda series.
- 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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1291815f88190a6eaf73e444dc1c2 |
completed | April 28, 2026, 9:39 p.m. |
Created at: April 16, 2026, 8:30 p.m.