Triple
T17679238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Def Comedy Jam |
E440722
|
entity |
| Predicate | featuredPerformer |
P17435
|
FINISHED |
| Object | Joe Torry |
—
|
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: Joe Torry | Statement: [Def Comedy Jam, featuredPerformer, Joe Torry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joe Torry Context triple: [Def Comedy Jam, featuredPerformer, Joe Torry]
-
A.
Joe Torry
chosen
Joe Torry is an American actor and comedian best known for his roles in 1990s films and television, including a notable performance in the romantic drama "Poetic Justice."
-
B.
Terry McCaleb
Terry McCaleb is a retired FBI profiler and heart transplant recipient who becomes an unlikely investigator in Michael Connelly’s crime novel "Blood Work."
-
C.
Jim Tunney
Jim Tunney is a former NFL official renowned as one of the league’s most respected referees, often called the “Dean of NFL Referees.”
-
D.
Kevin Garvey
Kevin Garvey is the troubled small-town police chief and central protagonist of the television drama "The Leftovers," known for his psychological struggles amid a mysteriously altered world.
-
E.
Alan Osbiston
Alan Osbiston was a British film editor known for his work on notable mid-20th-century films, including major war and drama productions.
- 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46f6f8054819087b2fe9bc8ad8d2f |
completed | April 19, 2026, 6 a.m. |
Created at: April 10, 2026, 10:01 a.m.