Triple
T13854530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hell or High Water (2016 film) |
E333028
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Julie Yorn |
E585572
|
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: Julie Yorn | Statement: [Hell or High Water (2016 film), producer, Julie Yorn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Julie Yorn Context triple: [Hell or High Water (2016 film), producer, Julie Yorn]
-
A.
Julie Yorn
chosen
Julie Yorn is an American film producer known for her work on a range of Hollywood movies, including comedies and thrillers.
-
B.
Jocelyn Ritchie
Jocelyn Ritchie is a musician best known for her collaborative work with American rock-rap artist Kid Rock.
-
C.
Jocelyn Lane
Jocelyn Lane is a British-born actress and model best known for her film roles in the 1950s and 1960s, including appearances in adventure and comedy movies.
-
D.
Jeannie Holland
Jeannie Holland is known as the wife of actor Tom Holland.
-
E.
Julie Grigio
Julie Grigio is the resilient human protagonist of the post-apocalyptic paranormal romance novel and film "Warm Bodies," who forms an unlikely bond with a zombie that challenges the boundaries between the living and the dead.
- 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_69d81c5ba13c8190839315f54768acfd |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02db9c9c81909bb2d2fbfb7394b1 |
completed | April 14, 2026, 9:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcd08c21a48190b4077d5acb4ab658 |
completed | May 7, 2026, 5:49 p.m. |
Created at: April 9, 2026, 10:14 p.m.