Triple
T19453566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grandma (2015 film) |
E486677
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | Jonathan Corn |
—
|
NE NERFINISHED |
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: Jonathan Corn | Statement: [Grandma (2015 film), editor, Jonathan Corn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jonathan Corn Context triple: [Grandma (2015 film), editor, Jonathan Corn]
-
A.
Jonathan Corn
chosen
Jonathan Corn is a film editor best known for his work on the science-fiction adventure movie "The Adam Project."
-
B.
Jon Kern
Jon Kern is a software engineer and consultant best known as one of the original co-authors of the Agile Manifesto, helping to shape modern agile software development practices.
-
C.
Justin Cornwell
Justin Cornwell is an American actor best known for starring opposite Bill Paxton in the television adaptation of "Training Day."
-
D.
Jon Bauman
Jon Bauman is an American musician and television personality best known for his role as "Bowzer" in the retro rock-and-roll group Sha Na Na.
-
E.
Jason Crouse
Jason Crouse is a fictional defense investigator and love interest of Alicia Florrick on the legal drama television series "The Good Wife."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6339407a08190a3e0213bfbb4df3d |
completed | April 20, 2026, 2:09 p.m. |
Created at: April 10, 2026, 1:38 p.m.