Triple
T21899605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Kunis |
E540771
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object | Elvira Kunis |
—
|
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: Elvira Kunis | Statement: [Mark Kunis, relative, Elvira Kunis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elvira Kunis Context triple: [Mark Kunis, relative, Elvira Kunis]
-
A.
Elvira Kunis
chosen
Elvira Kunis is best known as the mother of actress Mila Kunis and the wife of Mark Kunis.
-
B.
Mila Kunis
Mila Kunis is an American actress known for her roles in films like "Black Swan" and "Forgetting Sarah Marshall" and for voicing Meg Griffin on the animated series "Family Guy."
-
C.
Rande Gerber
Rande Gerber is an American businessman and former model best known as a nightlife industry entrepreneur and co-founder of the Casamigos tequila brand alongside George Clooney.
-
D.
Mena Suvari
Mena Suvari is an American actress and model best known for her breakout role in the acclaimed film "American Beauty" and her work in the "American Pie" series.
-
E.
Marilu Henner
Marilu Henner is an American actress and author best known for her role as Elaine Nardo on the TV sitcom "Taxi" and for her appearances in numerous film and television projects.
- 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_69e0c47b4e8c81908c8076eaa4c8e4f2 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f11fca2bf88190b2a5b912aa102513 |
completed | April 28, 2026, 8:59 p.m. |
Created at: April 16, 2026, 7:07 p.m.