Triple
T18826310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Count |
E460395
|
entity |
| Predicate | featuresActor |
P15562
|
FINISHED |
| Object | Leo White |
—
|
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: Leo White | Statement: [The Count, featuresActor, Leo White]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leo White Context triple: [The Count, featuresActor, Leo White]
-
A.
Leo White
chosen
Leo White was a British-born American character actor and director best known for his work in silent films, including frequent collaborations with Charlie Chaplin.
-
B.
Bibb Falk
Bibb Falk was an American baseball player and longtime University of Texas coach known for leading the Longhorns to multiple national championships and having the school's baseball stadium named in his honor.
-
C.
Gil Doud
Gil Doud was an American screenwriter best known for his work on mid-20th-century Hollywood films, particularly war and action dramas.
-
D.
David Kaye
David Kaye is a Canadian voice actor best known for his extensive work in animation and video games, including major roles in the Transformers franchise.
-
E.
Hans Conried
Hans Conried was an American character actor and voice actor best known for his comedic and often villainous roles in mid-20th-century film, radio, television, and animation.
- 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_69d8dcf94c288190a06dea029ae4b223 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a6bec7b08190b040ec8b3693f037 |
completed | April 20, 2026, 4:08 a.m. |
Created at: April 10, 2026, 11:56 a.m.