Triple
T17871812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reference and Contingency |
E446853
|
entity |
| Predicate | influencedBy |
P9
|
FINISHED |
| Object | David Kaplan |
—
|
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: David Kaplan | Statement: [Reference and Contingency, influencedBy, David Kaplan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Kaplan Context triple: [Reference and Contingency, influencedBy, David Kaplan]
-
A.
David Kaplan
chosen
David Kaplan is an American philosopher best known for his influential work in the philosophy of language and logic, particularly on demonstratives, indexicals, and the semantics of context.
-
B.
David Kaplan
David Kaplan is a film producer known for his work on independent and genre films, including serving as a producer on the movie "Kicks."
-
C.
Jonathan Kaplan
Jonathan Kaplan is an American film and television director best known for his work on the acclaimed 1988 courtroom drama "The Accused."
-
D.
Greg Kaplan
Greg Kaplan is an economist known for his research on household heterogeneity, consumption, and macroeconomic policy, and for his contributions to modern macroeconomic modeling.
-
E.
Aaron Kaplan
Aaron Kaplan is a television producer and executive known for developing and overseeing numerous network and cable series through his production company.
- 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_69d8b9f4c22c819093c2680434472894 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49aa30ff8819090c51c1d7767e952 |
completed | April 19, 2026, 9:04 a.m. |
Created at: April 10, 2026, 10:18 a.m.