Triple
T3210338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aleksandra Sokolovskaya |
E67262
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Sokolovskaya
Sokolovskaya is a Russian-language surname of Slavic origin, borne by various individuals from Russian-speaking regions.
|
E339308
|
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: Sokolovskaya | Statement: [Aleksandra Sokolovskaya, familyName, Sokolovskaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sokolovskaya Context triple: [Aleksandra Sokolovskaya, familyName, Sokolovskaya]
-
A.
Voykovskaya
Voykovskaya is a Moscow Metro station serving the Zamoskvoretskaya Line in the northern part of the city.
-
B.
Savyolovskaya
Savyolovskaya is a Moscow Metro station on the Big Circle Line, serving as part of the city’s modern orbital rapid transit network.
-
C.
Savyolovskaya
Savyolovskaya is a Moscow Metro station serving the Serpukhovsko–Timiryazevskaya Line in the northern part of the city.
-
D.
Dobryninskaya
Dobryninskaya is a Moscow Metro station on the circular Koltsevaya Line, known for its Stalinist-era architecture and central location.
-
E.
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.
- 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: Sokolovskaya Triple: [Aleksandra Sokolovskaya, familyName, Sokolovskaya]
Generated description
Sokolovskaya is a Russian-language surname of Slavic origin, borne by various individuals from Russian-speaking regions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sokolovskaya Target entity description: Sokolovskaya is a Russian-language surname of Slavic origin, borne by various individuals from Russian-speaking regions.
-
A.
Voykovskaya
Voykovskaya is a Moscow Metro station serving the Zamoskvoretskaya Line in the northern part of the city.
-
B.
Savyolovskaya
Savyolovskaya is a Moscow Metro station on the Big Circle Line, serving as part of the city’s modern orbital rapid transit network.
-
C.
Savyolovskaya
Savyolovskaya is a Moscow Metro station serving the Serpukhovsko–Timiryazevskaya Line in the northern part of the city.
-
D.
Dobryninskaya
Dobryninskaya is a Moscow Metro station on the circular Koltsevaya Line, known for its Stalinist-era architecture and central location.
-
E.
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.
- 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_69ad858ac36c81909962589cd277d6e2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaab886c48190b72e36d0ac855ffe |
completed | March 8, 2026, 4:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2771204e0819086ae2838a368589a |
completed | March 12, 2026, 8:19 a.m. |
| NEDg | Description generation | batch_69b27844c6708190ac61f00a74a2ef27 |
completed | March 12, 2026, 8:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b27911ff1481908a36f279a871c510 |
completed | March 12, 2026, 8:28 a.m. |
Created at: March 8, 2026, 3:07 p.m.