Triple
T21448469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. Eastwood & Company |
E529140
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Francesca Eastwood |
—
|
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: Francesca Eastwood | Statement: [Mrs. Eastwood & Company, starring, Francesca Eastwood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Francesca Eastwood Context triple: [Mrs. Eastwood & Company, starring, Francesca Eastwood]
-
A.
Francesca Eastwood
chosen
Francesca Eastwood is an American actress, model, and television personality, and the daughter of filmmaker Clint Eastwood.
-
B.
Kimber Lynn Eastwood
Kimber Lynn Eastwood is an American film producer, makeup artist, and the daughter of actor-director Clint Eastwood.
-
C.
Alison Eastwood
Alison Eastwood is an American actress, director, and fashion model, and the daughter of filmmaker Clint Eastwood.
-
D.
Rosanna Arquette
Rosanna Arquette is an American actress and filmmaker known for her roles in films such as "Desperately Seeking Susan" and "Pulp Fiction."
-
E.
Kathryn Eastwood
Kathryn Eastwood is an American actress and screenwriter, best known as one of Clint Eastwood’s daughters and for her roles in several independent films.
- 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_69e0c457579481909db68053ed99750c |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9d11ca48190aafe25c97dfa5578 |
completed | April 23, 2026, 9:43 a.m. |
Created at: April 16, 2026, 6:06 p.m.