Triple
T21745809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Halloweentown II: Kalabar's Revenge |
E536783
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | Jon Cooksey |
—
|
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: Jon Cooksey | Statement: [Halloweentown II: Kalabar's Revenge, writer, Jon Cooksey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jon Cooksey Context triple: [Halloweentown II: Kalabar's Revenge, writer, Jon Cooksey]
-
A.
Jon Cooksey
chosen
Jon Cooksey is a television and film writer best known for co-writing the popular Disney Channel movie "Halloweentown."
-
B.
Donald Cooksey
Donald Cooksey was an American physicist known for his work in nuclear physics and his leadership role at the MIT Radiation Laboratory during World War II.
-
C.
Douglas Cook
Douglas Cook was an American screenwriter best known for co-writing action and thriller films such as "The Rock" and "Double Jeopardy."
-
D.
Rob Cook
Rob Cook is a renowned computer graphics researcher and Pixar executive known for his pioneering work in rendering and visual effects.
-
E.
Phil Cookson
Phil Cookson is a relatively obscure individual whose primary public mention appears to be as a namesake in reference data, with no widely documented achievements or roles.
- 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_69e0c46df5448190b4322127ffc4c690 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01a76540c8190b91a67f4a70869fb |
completed | April 28, 2026, 2:24 a.m. |
Created at: April 16, 2026, 6:49 p.m.