Triple
T12358284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Very Harold & Kumar 3D Christmas |
E294666
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Eric Kissack |
E348268
|
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: Eric Kissack | Statement: [A Very Harold & Kumar 3D Christmas, editedBy, Eric Kissack]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eric Kissack Context triple: [A Very Harold & Kumar 3D Christmas, editedBy, Eric Kissack]
-
A.
Eric Kissack
chosen
Eric Kissack is an American film editor and director known for his work on feature films and television comedies.
-
B.
Alex Kerner
Alex Kerner is the idealistic young protagonist of the German film "Good Bye, Lenin!", who stages an elaborate ruse to protect his fragile mother from learning about the fall of East Germany.
-
C.
Eric Pleskow
Eric Pleskow was an Austrian-born American film executive and producer best known for leading major studios and co-founding the influential independent film company Orion Pictures.
-
D.
Michael Kessler
Michael Kessler is a German actor and comedian known for his work in film, television, and sketch comedy.
-
E.
Jay Klaitz
Jay Klaitz is an American actor known for his work in theater, film, and television, including roles in Broadway productions and various character parts on screen.
- 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_69d6ab6d8a4081908636601e69ddf262 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f8e64dc81908c2242c68cd1b86e |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f68ea215a4819090cea3184f3a231c |
completed | May 2, 2026, 11:54 p.m. |
Created at: April 8, 2026, 9:54 p.m.