Triple
T6248898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sonya Levien |
E139994
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Sonya Levien |
E139994
|
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: Sonya Levien | Statement: [Sonya Levien, name, Sonya Levien]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sonya Levien Context triple: [Sonya Levien, name, Sonya Levien]
-
A.
Sonya Levien
chosen
Sonya Levien was a prominent American screenwriter known for her work on numerous Hollywood films from the silent era through the 1950s, often adapting literary and theatrical works for the screen.
-
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.
Tatiana Schlossberg
Tatiana Schlossberg is an American journalist and author, known for her environmental reporting and as a member of the Kennedy family.
-
D.
Elka Ostrovsky
Elka Ostrovsky is a sharp-tongued, eccentric elderly woman and main character on the sitcom "Hot in Cleveland," portrayed by Betty White.
-
E.
Anita Goshkin
Anita Goshkin was the first wife of Nobel Prize–winning American novelist Saul Bellow.
- 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_69c008b4858c819095b0199114a9a87b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0633c5f2081909b0246e061f8a7d9 |
completed | March 22, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5e3ef699c819090b5c21700d1deb6 |
completed | March 27, 2026, 1:57 a.m. |
Created at: March 22, 2026, 4:24 p.m.