Triple
T20478499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carmen |
E502386
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object | Meg Kasdan |
—
|
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: Meg Kasdan | Statement: [Carmen, creator, Meg Kasdan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meg Kasdan Context triple: [Carmen, creator, Meg Kasdan]
-
A.
Meg Kasdan
chosen
Meg Kasdan is an American filmmaker and screenwriter known for her collaborative work with her husband, director Lawrence Kasdan, on several acclaimed films.
-
B.
Lisa Bluder
Lisa Bluder is a longtime head coach of the University of Iowa women's basketball team, known for leading the Hawkeyes to national prominence behind star players like Caitlin Clark.
-
C.
Judi Martino
Judi Martino is best known as the wife of American traditional pop singer and actor Al Martino.
-
D.
Jo Eisinger
Jo Eisinger was an American screenwriter best known for his dark, psychologically complex film noir scripts, including classics like "Gilda" and "Night and the City."
-
E.
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."
- 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_69e0b4af32848190aea80682b44d5d6e |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69b54c8188190a71e35fab8d194a6 |
completed | April 20, 2026, 9:32 p.m. |
Created at: April 16, 2026, 11:34 a.m.