Triple
T6139967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scott Frank |
E136935
|
entity |
| Predicate | wroteScreenplayFor |
P15305
|
FINISHED |
| Object | Get Shorty |
E344122
|
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: Get Shorty | Statement: [Scott Frank, wroteScreenplayFor, Get Shorty]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Get Shorty Context triple: [Scott Frank, wroteScreenplayFor, Get Shorty]
-
A.
Get Shorty
Get Shorty is a 1995 crime-comedy film, based on Elmore Leonard’s novel, that follows a Miami loan shark navigating Hollywood’s movie business.
-
B.
Get Shorty (film)
chosen
Get Shorty (film) is a 1995 crime-comedy movie about a smooth-talking loan shark navigating Hollywood, based on Elmore Leonard’s novel of the same name.
-
C.
Shorty
Shorty is a lesser-known musical act connected to the experimental rock band U.S. Maple, likely sharing its avant-garde, noise-influenced style.
-
D.
Getlink
Getlink is a French-based company that owns and operates the infrastructure of the Channel Tunnel rail link between the United Kingdom and continental Europe.
-
E.
K-short
K-short is the short-lived neutral kaon, a subatomic meson that decays rapidly via the weak interaction and plays a key role in studies of CP violation.
- 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_69c008a179388190a3b5a081bbf46d55 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05cb030fc8190b78e4967eea65611 |
completed | March 22, 2026, 9:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c14173d2288190920b719e221a929c |
completed | March 23, 2026, 1:34 p.m. |
Created at: March 22, 2026, 4:15 p.m.