Triple
T16569263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kropinski |
E402541
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Kasha Kropinski |
E84889
|
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: Kasha Kropinski | Statement: [Kropinski, hasNotableBearer, Kasha Kropinski]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kasha Kropinski Context triple: [Kropinski, hasNotableBearer, Kasha Kropinski]
-
A.
Kasha Kropinski
chosen
Kasha Kropinski is a South African-born actress best known for her role as Ruth Cole on the American Western television series "Hell on Wheels."
-
B.
Renata Kallosh
Renata Kallosh is a theoretical physicist known for her influential work in supergravity, string theory, and cosmology.
-
C.
Salka Viertel
Salka Viertel was an Austrian-American screenwriter and actress best known for her collaborations with Greta Garbo on several classic Hollywood films.
-
D.
Antonina Korsak
Antonina Korsak was the wife of renowned Polish pianist, composer, and statesman Ignacy Jan Paderewski.
-
E.
Dania Krupska
Dania Krupska was an American choreographer and dancer best known for her work on mid-20th-century Broadway musicals.
- 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_69d8838648088190acf97ef11fc3f61b |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e35773c00c819091731bebc02a69bb |
completed | April 18, 2026, 10:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006ee616a0819089b3cdc1da951735 |
completed | May 10, 2026, 11:41 a.m. |
Created at: April 10, 2026, 5:16 a.m.