Triple
T17747501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Topher Brink |
E443025
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Fran Kranz |
—
|
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: Fran Kranz | Statement: [Topher Brink, portrayedBy, Fran Kranz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fran Kranz Context triple: [Topher Brink, portrayedBy, Fran Kranz]
-
A.
Fran Kranz
chosen
Fran Kranz is an American actor best known for his roles in the TV series "Dollhouse" and the horror-comedy film "The Cabin in the Woods."
-
B.
Thomas Sadoski
Thomas Sadoski is an American actor known for his roles in television series like "The Newsroom" and films such as "John Wick" and "Wild."
-
C.
Ben Schwartz
Ben Schwartz is an American actor, comedian, and voice performer known for roles in projects like Parks and Recreation and for voicing animated characters in films and television.
-
D.
Kyle Howard
Kyle Howard is an American actor best known for his comedic roles in television series and films, including prominent parts in sitcoms.
-
E.
Kyle Dunnigan
Kyle Dunnigan is an American comedian, actor, and writer known for his sketch work, stand-up, and frequent collaborations with Amy Schumer.
- 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_69d8b9ed3a2081909b2ec0d4dd2f4c37 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47ad33160819093c9bbd3c8957314 |
completed | April 19, 2026, 6:48 a.m. |
Created at: April 10, 2026, 10:10 a.m.