Triple
T10870983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kushner |
E256650
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Rachel Kushner |
E809498
|
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: Rachel Kushner | Statement: [Kushner, hasNotableBearer, Rachel Kushner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rachel Kushner Context triple: [Kushner, hasNotableBearer, Rachel Kushner]
-
A.
Rachel Kushner
chosen
Rachel Kushner is an American novelist and essayist known for critically acclaimed works such as "Telex from Cuba," "The Flamethrowers," and "The Mars Room."
-
B.
Stacy Schiff
Stacy Schiff is a Pulitzer Prize–winning American biographer and essayist known for acclaimed works on figures such as Cleopatra, Vera Nabokov, and the Salem witch trials.
-
C.
Anne Napolitano
Anne Napolitano is a character from the film "The Fisher King," involved in the story’s exploration of loneliness, connection, and redemption.
-
D.
Lionel Shriver
Lionel Shriver is an American author best known for her provocative, psychologically incisive novels such as "We Need to Talk About Kevin."
-
E.
Lauren Groff
Lauren Groff is an acclaimed contemporary American novelist and short story writer known for works such as "Fates and Furies," "Matrix," and "Florida."
- 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_69d6aa83d1448190a66d93c32394d21f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75186e75c8190bf046ea666faff54 |
completed | April 9, 2026, 7:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dff7dbea28819083511b56bcd7d4ce |
completed | April 15, 2026, 8:41 p.m. |
Created at: April 8, 2026, 9:20 p.m.