Triple
T23036977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Over the Hedge |
E573626
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object | Michael Fry |
—
|
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: Michael Fry | Statement: [Over the Hedge, creator, Michael Fry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Fry Context triple: [Over the Hedge, creator, Michael Fry]
-
A.
Michael Fry
chosen
Michael Fry is a cartoonist and writer best known as the co-creator of the comic strip "Over the Hedge," which was adapted into a popular animated film.
-
B.
Michael J. Nelson
Michael J. Nelson is an American writer, comedian, and actor best known as the head writer and host of the cult television series "Mystery Science Theater 3000."
-
C.
Eric Fry
Eric Fry is a financial analyst and investment strategist known for his work in macroeconomic research and long-term stock market forecasting.
-
D.
Don Frye
Don Frye is an American mixed martial artist, professional wrestler, and actor known for his tough-guy persona and roles in action and genre films.
-
E.
Paul F. Tompkins
Paul F. Tompkins is an American comedian, actor, and writer known for his stand-up, podcast appearances, and character roles in television and film.
- 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_69e245b911188190bc3d96326c847969 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1850fe5348190b42259595d82cff4 |
completed | April 29, 2026, 4:12 a.m. |
Created at: April 17, 2026, 3:53 p.m.