Triple
T7398823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yulia Navalnaya |
E170693
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Navalnaya
Navalnaya is the surname of Yulia Navalnaya, a Russian public figure and the widow of opposition leader Alexei Navalny.
|
E665393
|
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: Navalnaya | Statement: [Yulia Navalnaya, familyName, Navalnaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Navalnaya Context triple: [Yulia Navalnaya, familyName, Navalnaya]
-
A.
Sevastopolskaya
Sevastopolskaya is a Moscow Metro station serving the Serpukhovsko–Timiryazevskaya Line in the city’s southern part.
-
B.
Krasnoufimsk
Krasnoufimsk is a small historic town in Russia’s Ural region, known for its traditional architecture and role as a local administrative and cultural center.
-
C.
Vyatskoye
Vyatskoye is a rural locality in Russia’s Khabarovsk Krai, historically noted as the birthplace of North Korean leader Kim Jong Il.
-
D.
Malaya Nevka
Malaya Nevka is a distributary channel of the Neva River in Saint Petersburg, Russia, forming part of the city’s intricate river and canal network.
-
E.
Komsomolskaya
Komsomolskaya is one of Moscow Metro’s most famous and ornate stations, renowned for its grand Baroque-style decor and elaborate mosaics.
- 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: Navalnaya Triple: [Yulia Navalnaya, familyName, Navalnaya]
Generated description
Navalnaya is the surname of Yulia Navalnaya, a Russian public figure and the widow of opposition leader Alexei Navalny.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Navalnaya Target entity description: Navalnaya is the surname of Yulia Navalnaya, a Russian public figure and the widow of opposition leader Alexei Navalny.
-
A.
Sevastopolskaya
Sevastopolskaya is a Moscow Metro station serving the Serpukhovsko–Timiryazevskaya Line in the city’s southern part.
-
B.
Krasnoufimsk
Krasnoufimsk is a small historic town in Russia’s Ural region, known for its traditional architecture and role as a local administrative and cultural center.
-
C.
Vyatskoye
Vyatskoye is a rural locality in Russia’s Khabarovsk Krai, historically noted as the birthplace of North Korean leader Kim Jong Il.
-
D.
Malaya Nevka
Malaya Nevka is a distributary channel of the Neva River in Saint Petersburg, Russia, forming part of the city’s intricate river and canal network.
-
E.
Komsomolskaya
Komsomolskaya is one of Moscow Metro’s most famous and ornate stations, renowned for its grand Baroque-style decor and elaborate mosaics.
- 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_69c68a5f04188190ac266569c9280347 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f24c1c208190a3d11e816888760d |
completed | March 27, 2026, 9:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c82775d1188190bcf158da5a02b6e0 |
completed | March 28, 2026, 7:09 p.m. |
| NEDg | Description generation | batch_69c828c8b0588190a5a99380dc25d837 |
completed | March 28, 2026, 7:15 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8296962b48190b9f5cc4a66b93b91 |
completed | March 28, 2026, 7:18 p.m. |
Created at: March 27, 2026, 3:09 p.m.