Triple
T20859686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brian Henson |
E513581
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Mia Sara |
—
|
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: Mia Sara | Statement: [Brian Henson, spouse, Mia Sara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mia Sara Context triple: [Brian Henson, spouse, Mia Sara]
-
A.
Mia Sara
chosen
Mia Sara is an American actress best known for her role as Sloane Peterson in the 1986 teen comedy film "Ferris Bueller's Day Off."
-
B.
Mia Morgan
Mia Morgan is a central character in the romantic comedy-drama film "The Best Man," around whom much of the story’s interpersonal conflict and emotional tension revolves.
-
C.
Mia Sutton
Mia Sutton is a central character in the 2017 live-action adaptation of "Death Note," portrayed as a high school student whose ruthless ambition and fascination with the deadly notebook drive much of the film’s dark plot.
-
D.
Mia Nadasi
Mia Nadasi is known as the wife of Hungarian-born British film and television director Peter Medak.
-
E.
Mia Serafino
Mia Serafino is an American actress known for her work in film and television, including roles in independent movies and network sitcoms.
- 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_69e0b4f5b01081909452f654d2fc3f50 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c3aabef4819098f0fd24dcc27dbd |
completed | April 21, 2026, 12:24 a.m. |
Created at: April 16, 2026, 12:44 p.m.