Triple
T23406423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Price of Salt |
E559946
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Patricia Highsmith |
—
|
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: Patricia Highsmith | Statement: [The Price of Salt, author, Patricia Highsmith]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Patricia Highsmith Context triple: [The Price of Salt, author, Patricia Highsmith]
-
A.
Patricia Highsmith
chosen
Patricia Highsmith was an American novelist best known for her psychologically complex crime and suspense fiction, including the Ripley series.
-
B.
Sally Kellerman
Sally Kellerman was an American actress and singer best known for her Oscar-nominated role as Major Margaret "Hot Lips" Houlihan in the film MASH.
-
C.
Mary Higgins Clark
Mary Higgins Clark was a bestselling American author renowned for her suspenseful mystery and thriller novels, often featuring strong female protagonists.
-
D.
Sue Grafton
Sue Grafton was an American mystery writer best known for her alphabet-titled Kinsey Millhone detective novels.
-
E.
Paula Hawkins
Paula Hawkins is a British author best known for her psychological thriller novel "The Girl on the Train," which was adapted into the 2016 film of the same name.
- 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_69e2454b3a5881909c64773dc8a5d289 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1a50e607c8190ba0a22e89862a2d9 |
completed | April 29, 2026, 6:28 a.m. |
Created at: April 17, 2026, 5:38 p.m.