Triple
T10752223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Down with Love |
E253597
|
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: [Down with Love, producer, Dan Jinks]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Jinks Context triple: [Down with Love, 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 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.
-
C.
Eric Danchick
Eric Danchick is a film producer known for his work on the movie "Bound 2."
-
D.
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."
-
E.
Mike Krieger
Mike Krieger is a Brazilian-American entrepreneur and software engineer best known as the co-founder and former CTO of the photo-sharing social media platform Instagram.
- 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_69d6aa5e51e8819095f06881cecf152e |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d71dc184d0819085f8bc4edb034377 |
completed | April 9, 2026, 3:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e1548affbc8190bbca099f8c8d4910 |
completed | April 16, 2026, 9:28 p.m. |
Created at: April 8, 2026, 9:15 p.m.