Triple
T13013110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosalyn Tureck |
E322472
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Rosalyn |
E994172
|
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: Rosalyn | Statement: [Rosalyn Tureck, givenName, Rosalyn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosalyn Context triple: [Rosalyn Tureck, givenName, Rosalyn]
-
A.
Rosalyn
chosen
Rosalyn is a feminine given name, often considered a variant of Rosalind or Rose, typically associated with meanings related to "rose" and beauty.
-
B.
Mary Ruth
Mary Ruth is a fictional character featured in the American television sitcom "The Debbie Reynolds Show."
-
C.
Ruth Rose
Ruth Rose was an American screenwriter best known for co-writing the classic 1933 monster film "King Kong."
-
D.
Marilynne
Marilynne is the given name of Marilynne Robinson, the acclaimed American novelist and essayist known for works such as "Housekeeping" and the "Gilead" series.
-
E.
Diane
Diane is a feminine given name of Latin origin, derived from the name of the Roman goddess Diana.
- 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_69d807657e8c8190bd9435ee2f823845 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97ecbb8f4819094d55eb07cb5ad97 |
completed | April 10, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6ff00a2f48190b88babba80521818 |
completed | May 3, 2026, 7:53 a.m. |
Created at: April 9, 2026, 8:49 p.m.