Triple
T16182298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rush Hour 3 |
E392713
|
entity |
| Predicate | storyBy |
P1955
|
FINISHED |
| Object | Jeff Nathanson |
E318412
|
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: Jeff Nathanson | Statement: [Rush Hour 3, storyBy, Jeff Nathanson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeff Nathanson Context triple: [Rush Hour 3, storyBy, Jeff Nathanson]
-
A.
Jeff Nathanson
chosen
Jeff Nathanson is an American screenwriter and film director best known for writing high-profile Hollywood films such as "Catch Me If You Can," "The Terminal," and Disney's live-action "The Lion King."
-
B.
Michael Nathanson
Michael Nathanson is a film producer known for his work on major Hollywood movies, including the legal drama "A Time to Kill."
-
C.
Brent Judd
Brent Judd is a film and television producer best known for his work on the comedy series "Trainwreck."
-
D.
Greg Latter
Greg Latter is a screenwriter best known for his work on the apartheid-era drama film "Goodbye Bafana."
-
E.
Mike Lilley
Mike Lilley is known as the husband of American sports broadcaster and television personality Melissa Stark.
- 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_69d87f1e49ac8190a311b54d32990576 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e2205d858c8190802d44e08e3cdcd6 |
completed | April 17, 2026, 11:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a007d9a8d7481908c7bc4711ddacc13 |
completed | May 10, 2026, 12:44 p.m. |
Created at: April 10, 2026, 5:02 a.m.