Triple
T17095138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carly Tenney |
E414826
|
entity |
| Predicate | hasMarriageWith |
P45367
|
FINISHED |
| Object | Mike Kasnoff |
—
|
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: Mike Kasnoff | Statement: [Carly Tenney, hasMarriageWith, Mike Kasnoff]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Kasnoff Context triple: [Carly Tenney, hasMarriageWith, Mike Kasnoff]
-
A.
Mike Kasnoff
chosen
Mike Kasnoff is a fictional character from the soap opera "As the World Turns," known for his romantic entanglements and dramatic storylines in Oakdale.
-
B.
Alex Casnoff
Alex Casnoff is an American musician and keyboardist best known for his work with the indie rock band Dawes and later projects like Harriet.
-
C.
Michael Filerman
Michael Filerman was an American television producer best known for developing and producing popular prime-time soap operas during the 1970s and 1980s.
-
D.
Josh Kesselman
Josh Kesselman is a film and television producer best known for his work as an executive producer on projects such as the series "The Great."
-
E.
Mitch Kertzman
Mitch Kertzman is an American technology executive and entrepreneur best known for his leadership roles in the software and semiconductor industries, including at companies like LSI Logic and Sybase.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbfc9158819081689d3d594a1908 |
completed | April 18, 2026, 7:31 p.m. |
Created at: April 10, 2026, 5:35 a.m.