Triple
T23049592
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Will Sasso |
E573970
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Will Sasso |
—
|
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: Will Sasso | Statement: [Will Sasso, name, Will Sasso]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Will Sasso Context triple: [Will Sasso, name, Will Sasso]
-
A.
Will Sasso
chosen
Will Sasso is a Canadian actor and comedian best known for his sketch comedy work on television and his roles in film and voice acting.
-
B.
Del Sasser
"Del Sasser" is a jazz composition best known through its recording by the Cannonball Adderley Quintet on the album "Them Dirty Blues."
-
C.
Michael Sarnoski
Michael Sarnoski is an American filmmaker and screenwriter best known for directing the acclaimed drama "Pig" and later helming the horror prequel "A Quiet Place: Day One."
-
D.
Mel Dinelli
Mel Dinelli was an American playwright and screenwriter best known for his work in film noir and suspense films during the mid-20th century.
-
E.
Greg Stillson
Greg Stillson is the ambitious, populist politician and primary antagonist in Stephen King’s novel "The Dead Zone," whose rise to power is foreseen to lead to catastrophic consequences.
- 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_69e245b9c11481909d06c872214d21af |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1867b800881909fabf9dca994c9e7 |
completed | April 29, 2026, 4:18 a.m. |
Created at: April 17, 2026, 3:54 p.m.