Triple
T12445667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benjamin Kubelsky |
E297390
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Kubelsky |
E297390
|
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: Kubelsky | Statement: [Benjamin Kubelsky, familyName, Kubelsky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kubelsky Context triple: [Benjamin Kubelsky, familyName, Kubelsky]
-
A.
Kubelsky
chosen
Kubelsky is the original surname of American comedian and entertainer Jack Benny, born Benjamin Kubelsky.
-
B.
Kropinski
Kropinski is a surname most notably associated with South African-born actress Kasha Kropinski.
-
C.
Kobeliaky
Kobeliaky is a historic town in central Ukraine, located in Poltava Oblast along the Vorskla River.
-
D.
Kubek
Kubek is the surname of Tony Kubek, a former Major League Baseball shortstop and longtime television sportscaster best known for his years with the New York Yankees.
-
E.
Kuppenheimer
Kuppenheimer was a prominent American men's clothing company best known for its high-quality suits and influential early 20th-century advertising campaigns.
- 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_69d6ada166c48190b902972cd2408fa3 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d90f18c819083a36ff4b9be4a20 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63f132d048190b58a8381bdc74cad |
completed | May 2, 2026, 6:14 p.m. |
Created at: April 8, 2026, 9:55 p.m.