Triple
T10527663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Balls of Fury |
E248347
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Dan Fogler |
E514074
|
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: Dan Fogler | Statement: [Balls of Fury, stars, Dan Fogler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Fogler Context triple: [Balls of Fury, stars, Dan Fogler]
-
A.
Dan Fogler
chosen
Dan Fogler is an American actor and comedian best known for roles in films like "Fantastic Beasts" and "Balls of Fury" as well as his work on stage and in voice acting.
-
B.
Zach Woods
Zach Woods is an American actor and comedian best known for his roles on television series such as "The Office," "Silicon Valley," and "Avenue 5."
-
C.
Mitch Robbins
Mitch Robbins is the neurotic, middle-aged New Yorker who embarks on a life-changing cattle drive in the comedy film "City Slickers."
-
D.
Jonah Hill
Jonah Hill is an American actor, comedian, and filmmaker known for his roles in films such as Superbad, Moneyball, and The Wolf of Wall Street.
-
E.
David Franco
David Franco is a cinematographer known for his work on the film "Boycott."
- 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_69d381c5c7448190bec34bee7ec72bac |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509f5ec348190875c8c877e70ba4a |
completed | April 7, 2026, 1:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d90e31350c8190a7493cdc33cc450a |
completed | April 10, 2026, 2:50 p.m. |
Created at: April 6, 2026, 12:30 p.m.