Triple
T8271345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tatiana Tarasova |
E193435
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Tarasova
Tarasova is a Russian surname most prominently associated with Tatiana Tarasova, a renowned figure skating coach and former competitor.
|
E726354
|
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: Tarasova | Statement: [Tatiana Tarasova, familyName, Tarasova]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tarasova Context triple: [Tatiana Tarasova, familyName, Tarasova]
-
A.
Paveletskaya
Paveletskaya is a Moscow Metro station named after the nearby Paveletsky railway terminal, serving as a key transport hub in the city’s network.
-
B.
Volkova
Volkova is a Russian surname commonly borne by individuals of Slavic origin, including notable figures in politics, arts, and sciences.
-
C.
Voykovskaya
Voykovskaya is a Moscow Metro station serving the Zamoskvoretskaya Line in the northern part of the city.
-
D.
Veshenskaya
Veshenskaya is a rural Cossack stanitsa in Russia’s Rostov Oblast, best known as the home village of Nobel Prize–winning writer Mikhail Sholokhov and the setting for much of his work.
-
E.
Dobryninskaya
Dobryninskaya is a Moscow Metro station on the circular Koltsevaya Line, known for its Stalinist-era architecture and central location.
- 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: Tarasova Triple: [Tatiana Tarasova, familyName, Tarasova]
Generated description
Tarasova is a Russian surname most prominently associated with Tatiana Tarasova, a renowned figure skating coach and former competitor.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tarasova Target entity description: Tarasova is a Russian surname most prominently associated with Tatiana Tarasova, a renowned figure skating coach and former competitor.
-
A.
Paveletskaya
Paveletskaya is a Moscow Metro station named after the nearby Paveletsky railway terminal, serving as a key transport hub in the city’s network.
-
B.
Volkova
Volkova is a Russian surname commonly borne by individuals of Slavic origin, including notable figures in politics, arts, and sciences.
-
C.
Voykovskaya
Voykovskaya is a Moscow Metro station serving the Zamoskvoretskaya Line in the northern part of the city.
-
D.
Veshenskaya
Veshenskaya is a rural Cossack stanitsa in Russia’s Rostov Oblast, best known as the home village of Nobel Prize–winning writer Mikhail Sholokhov and the setting for much of his work.
-
E.
Dobryninskaya
Dobryninskaya is a Moscow Metro station on the circular Koltsevaya Line, known for its Stalinist-era architecture and central location.
- 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_69ca82e14ae481908ffdb822cd2192bc |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7986f8cc8190a529dda980dd6e98 |
completed | March 31, 2026, 7:36 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd9510a74c8190a8f8c9c7e430b5ae |
completed | April 1, 2026, 9:58 p.m. |
| NEDg | Description generation | batch_69cdab59ac188190ac017651b5a9a04a |
completed | April 1, 2026, 11:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cdb2ae376c8190b3918ba6b269dba9 |
completed | April 2, 2026, 12:05 a.m. |
Created at: March 30, 2026, 5:50 p.m.