Triple
T19952261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nona Balakian Citation |
E479588
|
entity |
| Predicate | notableRecipient |
P108
|
FINISHED |
| Object | Michiko Kakutani |
—
|
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: Michiko Kakutani | Statement: [Nona Balakian Citation, notableRecipient, Michiko Kakutani]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michiko Kakutani Context triple: [Nona Balakian Citation, notableRecipient, Michiko Kakutani]
-
A.
Michiko Kakutani
chosen
Michiko Kakutani is an influential American literary critic best known for her long tenure as chief book critic for The New York Times.
-
B.
Paul Genzlinger
Paul Genzlinger is a mild-mannered, somewhat awkward music teacher who briefly dates Jessica Day on the TV sitcom "New Girl."
-
C.
Richard Brody
Richard Brody is an American film critic and author best known for his work at The New Yorker and his writings on French cinema, particularly Jean-Luc Godard.
-
D.
Michael Kimmelman
Michael Kimmelman is an American architecture critic and journalist best known for his work at The New York Times.
-
E.
David Goodis
David Goodis was an American noir and hardboiled crime novelist known for his bleak, psychologically driven stories of down-and-out characters.
- 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_69d8e522a17c819095165d4d24939fd8 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65a6c87388190a1bada3117acaf7b |
completed | April 20, 2026, 4:55 p.m. |
Created at: April 10, 2026, 1:54 p.m.