Triple
T11405255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elmira Abdrazakova |
E270219
|
entity |
| Predicate | awardReceived |
P11
|
FINISHED |
| Object |
Miss Russia 2013
Miss Russia 2013 is a national beauty pageant title awarded to the winner of Russia’s premier annual beauty contest.
|
E923912
|
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: Miss Russia 2013 | Statement: [Elmira Abdrazakova, awardReceived, Miss Russia 2013]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miss Russia 2013 Context triple: [Elmira Abdrazakova, awardReceived, Miss Russia 2013]
-
A.
Elle Russia
Elle Russia is the Russian-language edition of the international fashion and lifestyle magazine Elle, featuring content on style, beauty, culture, and celebrity.
-
B.
Makarova
Makarova is a Russian surname most prominently associated with the celebrated ballerina and choreographer Natalia Makarova.
-
C.
Sochi
Sochi is a Russian resort city on the Black Sea coast, known for its subtropical climate, beaches, and as the host of the 2014 Winter Olympics.
-
D.
Irina
Irina is a feminine given name commonly used in Slavic and other Eastern European cultures, derived from the Greek name Irene meaning "peace."
-
E.
Sofia Vassilieva
Sofia Vassilieva is an American actress best known for her roles in the TV series "Medium" and the film "My Sister's Keeper."
- 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: Miss Russia 2013 Triple: [Elmira Abdrazakova, awardReceived, Miss Russia 2013]
Generated description
Miss Russia 2013 is a national beauty pageant title awarded to the winner of Russia’s premier annual beauty contest.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Miss Russia 2013 Target entity description: Miss Russia 2013 is a national beauty pageant title awarded to the winner of Russia’s premier annual beauty contest.
-
A.
Elle Russia
Elle Russia is the Russian-language edition of the international fashion and lifestyle magazine Elle, featuring content on style, beauty, culture, and celebrity.
-
B.
Makarova
Makarova is a Russian surname most prominently associated with the celebrated ballerina and choreographer Natalia Makarova.
-
C.
Sochi
Sochi is a Russian resort city on the Black Sea coast, known for its subtropical climate, beaches, and as the host of the 2014 Winter Olympics.
-
D.
Irina
Irina is a feminine given name commonly used in Slavic and other Eastern European cultures, derived from the Greek name Irene meaning "peace."
-
E.
Sofia Vassilieva
Sofia Vassilieva is an American actress best known for her roles in the TV series "Medium" and the film "My Sister's Keeper."
- 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_69d6aaddeaa8819088b30ef7b50598c9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8014c820c81908538ba4a08e13230 |
completed | April 9, 2026, 7:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e58d56608481908dbb19daa2abfc0a |
completed | April 20, 2026, 2:20 a.m. |
| NEDg | Description generation | batch_69e59777b1208190a33a50da286535ee |
completed | April 20, 2026, 3:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e5a3cf9d388190944340af484b3a54 |
completed | April 20, 2026, 3:55 a.m. |
Created at: April 8, 2026, 9:34 p.m.