Triple
T20044372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saul Bellow |
E497516
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Anita Goshkin |
—
|
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: Anita Goshkin | Statement: [Saul Bellow, spouse, Anita Goshkin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anita Goshkin Context triple: [Saul Bellow, spouse, Anita Goshkin]
-
A.
Anita Goshkin
chosen
Anita Goshkin was the first wife of Nobel Prize–winning American novelist Saul Bellow.
-
B.
Sonya Kalish
Sonya Kalish, better known by her stage name Sophie Tucker, was a famed early 20th-century American singer and comedian celebrated as "The Last of the Red Hot Mamas."
-
C.
Valentina Brodsky
Valentina Brodsky was the second wife of renowned modernist painter Marc Chagall, with whom she spent his later years in France.
-
D.
Rufina Gurevich
Rufina Gurevich is a mathematician known for her work in areas influenced by Lev Pontryagin’s contributions to topology and control theory.
-
E.
Elka Ostrovsky
Elka Ostrovsky is a sharp-tongued, eccentric elderly woman and main character on the sitcom "Hot in Cleveland," portrayed by Betty White.
- 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_69da627278c88190babe4297a9df1236 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e662eea09481908d1001165e9d719c |
completed | April 20, 2026, 5:31 p.m. |
Created at: April 11, 2026, 3:37 p.m.