Triple
T22977902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sara Gruen |
E571375
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Sara Gruen |
—
|
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: Sara Gruen | Statement: [Sara Gruen, name, Sara Gruen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sara Gruen Context triple: [Sara Gruen, name, Sara Gruen]
-
A.
Sara Gruen
chosen
Sara Gruen is a Canadian-American novelist best known for her bestselling historical novel "Water for Elephants."
-
B.
Rebecca Wells
Rebecca Wells is an American author best known for her bestselling novel "Divine Secrets of the Ya-Ya Sisterhood" and its related works exploring Southern women’s lives and friendships.
-
C.
Francine Rivers
Francine Rivers is a bestselling American author known for her inspirational Christian fiction novels, particularly "Redeeming Love."
-
D.
Sandra Grant
Sandra Grant is an American actress best known for her long-term marriage to legendary singer Tony Bennett.
-
E.
Cathryn Michon
Cathryn Michon is an American screenwriter, author, and filmmaker known for adapting bestselling novels such as "A Dog’s Journey" for the screen.
- 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_69e245b3c50481908bb3741ec9f40862 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18292f3788190ab4e9d559e0070c8 |
completed | April 29, 2026, 4:01 a.m. |
Created at: April 17, 2026, 3:49 p.m.