Triple
T15992787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karyn Kusama |
E387876
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Karyn |
E168870
|
NE FINISHED |
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: Karyn | Statement: [Karyn Kusama, givenName, Karyn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karyn Context triple: [Karyn Kusama, givenName, Karyn]
-
A.
Karyn
chosen
Karyn is a feminine given name, typically considered a modern or alternative spelling of Karen.
-
B.
Karyn Parsons
Karyn Parsons is an American actress best known for playing the snobbish yet lovable Hilary Banks on the hit 1990s sitcom "The Fresh Prince of Bel-Air."
-
C.
Kristin Burr
Kristin Burr is a film producer known for her work on major studio projects, including Disney live-action adaptations such as "Cruella."
-
D.
Carrie Krueger
Carrie Krueger is a ghost girl character from the animated series "The Amazing World of Gumball," known for her goth appearance and shy, deadpan personality.
-
E.
Kara Vallow
Kara Vallow is an American television producer best known for her work on animated series such as Family Guy and its related projects.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e15785347081908831b4cbc9a2dd45 |
completed | April 16, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3d3ef2881909213ff608192f1ef |
completed | May 9, 2026, 11:31 p.m. |
Created at: April 10, 2026, 4:54 a.m.