Triple
T12358278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Very Harold & Kumar 3D Christmas |
E294666
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Nathan Kahane |
E136662
|
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: Nathan Kahane | Statement: [A Very Harold & Kumar 3D Christmas, producer, Nathan Kahane]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nathan Kahane Context triple: [A Very Harold & Kumar 3D Christmas, producer, Nathan Kahane]
-
A.
Nathan Kahane
chosen
Nathan Kahane is a film producer and studio executive known for backing numerous successful Hollywood comedies and genre films.
-
B.
Ranaan Meyer
Ranaan Meyer is an American double bassist, composer, and founding member of the genre-blending string trio Time for Three.
-
C.
Nathan Resnick
Nathan Resnick is an American entrepreneur best known as the founder and CEO of Sourcify, a platform that helps companies streamline and manage their product manufacturing overseas.
-
D.
Jacob Goldman
Jacob Goldman is a supporting character in the comedy film "Grumpy Old Men," known as a friend and neighbor of the feuding protagonists in their small Minnesota town.
-
E.
Neil Kagan
Neil Kagan is an American editor and author known for producing richly illustrated historical and reference books, particularly for National Geographic.
- 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_69f634721e948190bb8e97ef677b9f59 |
completed | May 2, 2026, 5:29 p.m. |
Created at: April 8, 2026, 9:54 p.m.