Triple
T15997084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | American Beauty |
E387995
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Dan Jinks |
E387995
|
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: Dan Jinks | Statement: [American Beauty, producer, Dan Jinks]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Jinks Context triple: [American Beauty, producer, Dan Jinks]
-
A.
Dan Jinks
chosen
Dan Jinks is an American film and television producer best known for acclaimed movies such as "American Beauty" and "Big Fish."
-
B.
Steve Jinks
Steve Jinks is a former ATF agent with the unique ability to detect lies who becomes a key government artifact hunter on the science-fiction TV series "Warehouse 13."
-
C.
Steve Judd
Steve Judd is the aging, principled former lawman at the heart of the Western film "Ride the High Country," whose moral integrity drives the story’s central conflict.
-
D.
Eric Danchick
Eric Danchick is a film producer known for his work on the movie "Bound 2."
-
E.
Ken Jenkins
Ken Jenkins is an American actor best known for his role as the irascible hospital administrator Dr. Bob Kelso on the television series "Scrubs."
- 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e157882ef0819081143e530bd6413c |
completed | April 16, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004f39008c819095ad8512eb119ee8 |
completed | May 10, 2026, 9:26 a.m. |
Created at: April 10, 2026, 4:55 a.m.