Triple
T20199097
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Noise of Time |
E493163
|
entity |
| Predicate | notableCharacter |
P1481
|
FINISHED |
| Object | Nina Varzar |
—
|
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: Nina Varzar | Statement: [The Noise of Time, notableCharacter, Nina Varzar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nina Varzar Context triple: [The Noise of Time, notableCharacter, Nina Varzar]
-
A.
Nina Varzar
chosen
Nina Varzar was the wife of renowned Soviet composer Dmitri Shostakovich and a physicist by profession.
-
B.
Nina Eichinger
Nina Eichinger is a German television presenter and actress known for her work on various entertainment and music programs.
-
C.
Nora Zehetner
Nora Zehetner is an American actress known for her roles in films like "Brick" and the television series "Heroes."
-
D.
Anna Plochl
Anna Plochl was the commoner who became the morganatic wife of Archduke John of Austria, noted for their unconventional marriage across class lines in the 19th-century Habsburg Empire.
-
E.
Tatiana Schucht
Tatiana Schucht was an Italian-Russian revolutionary and close confidante of Antonio Gramsci, known for her role in supporting him during his imprisonment.
- 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_69da6269614c8190bb40475d9d477358 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66d8c0eac81908ffdf72d71d2e5d2 |
completed | April 20, 2026, 6:16 p.m. |
Created at: April 11, 2026, 11:37 p.m.