Triple
T15483543
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine Hicks |
E376981
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Kevin Yagher |
—
|
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: Kevin Yagher | Statement: [Catherine Hicks, spouse, Kevin Yagher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kevin Yagher Context triple: [Catherine Hicks, spouse, Kevin Yagher]
-
A.
Kevin Yagher
chosen
Kevin Yagher is an American special effects and makeup artist and director best known for his work on horror and fantasy films and for creating iconic genre characters.
-
B.
Ron Yerxa
Ron Yerxa is an American film producer known for his work on acclaimed independent and studio films such as "Little Miss Sunshine," "Election," and "Cold Mountain."
-
C.
Chris Sievernich
Chris Sievernich is a German film producer best known for his work on acclaimed art-house and independent films, including Wim Wenders’ "Paris, Texas."
-
D.
Kevin Hageman
Kevin Hageman is an American screenwriter and producer known for his work on animated and family films and television series, including contributions to The Lego Movie franchise.
-
E.
Kevin Manthei
Kevin Manthei is an American composer known for his work on film, television, and video game scores, particularly in the animation and superhero genres.
- 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f8e6ff08190b130b3a38f4190e7 |
completed | April 16, 2026, 1:46 a.m. |
Created at: April 10, 2026, 3:42 a.m.