Triple
T20541199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sol Kaplan |
E504336
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Sol 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: Sol Kaplan | Statement: [Sol Kaplan, name, Sol Kaplan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sol Kaplan Context triple: [Sol Kaplan, name, Sol Kaplan]
-
A.
Sol Kaplan
chosen
Sol Kaplan was an American composer best known for his film and television scores, including work in mid-20th-century Hollywood.
-
B.
Neil Kaplan
Neil Kaplan is an American voice actor best known for portraying heroic characters in animation and video games, including Optimus Prime in the "Transformers: Robots in Disguise" series.
-
C.
Billy Kaplan
Billy Kaplan is a Marvel Comics superhero, also known as Wiccan, who is a powerful magic user and a member of the Young Avengers.
-
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.
Hank Kaplan
Hank Kaplan is a fictional character from the American medical drama television series "Nurses."
- 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_69e0b4b476648190bc6019622ae54d3c |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a29224f081908298d104161c5bb9 |
completed | April 20, 2026, 10:02 p.m. |
Created at: April 16, 2026, 11:37 a.m.