Triple
T838420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valeri Kamensky |
E18122
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Kamensky
Kamensky is a Russian surname most notably associated with former professional ice hockey player Valeri Kamensky.
|
E100505
|
NE FINISHED |
How this triple was built (4 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: Kamensky | Statement: [Valeri Kamensky, familyName, Kamensky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kamensky Context triple: [Valeri Kamensky, familyName, Kamensky]
-
A.
Vasilevsky
Vasilevsky is a Russian surname most prominently associated with Aleksandr Vasilevsky, a leading Soviet military commander and Marshal of the Soviet Union during World War II.
-
B.
Blaustein
Blaustein is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, situated near the city of Ulm.
-
C.
Kasha Kropinski
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."
-
D.
Morgenstern
Morgenstern is a German surname borne by various notable figures in fields such as economics, literature, and the arts.
-
E.
Kadan
Kadan was a Mongol prince and military commander who played a key role in the Mongol invasions of Central and Eastern Europe in the 13th century.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kamensky Triple: [Valeri Kamensky, familyName, Kamensky]
Generated description
Kamensky is a Russian surname most notably associated with former professional ice hockey player Valeri Kamensky.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kamensky Target entity description: Kamensky is a Russian surname most notably associated with former professional ice hockey player Valeri Kamensky.
-
A.
Vasilevsky
Vasilevsky is a Russian surname most prominently associated with Aleksandr Vasilevsky, a leading Soviet military commander and Marshal of the Soviet Union during World War II.
-
B.
Blaustein
Blaustein is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, situated near the city of Ulm.
-
C.
Kasha Kropinski
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."
-
D.
Morgenstern
Morgenstern is a German surname borne by various notable figures in fields such as economics, literature, and the arts.
-
E.
Kadan
Kadan was a Mongol prince and military commander who played a key role in the Mongol invasions of Central and Eastern Europe in the 13th century.
- F. None of above. chosen
Provenance (5 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_69a49389f44881909a608fb27d89f247 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abd0e8bc8190afe29cd4745c2f86 |
completed | March 1, 2026, 9:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7929860f081909c86f84d7cfe6acb |
completed | March 4, 2026, 2:02 a.m. |
| NEDg | Description generation | batch_69a796370f388190b23cd19cc3fa5a3b |
completed | March 4, 2026, 2:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a796bee5388190ab0abf0bfa08ad97 |
completed | March 4, 2026, 2:19 a.m. |
Created at: March 1, 2026, 7:38 p.m.