Triple
T2807240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eudoxia Lopukhina |
E54081
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Lopukhina
Lopukhina is a Russian noble family name historically associated with Eudoxia Lopukhina, the first wife of Tsar Peter the Great.
|
E301505
|
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: Lopukhina | Statement: [Eudoxia Lopukhina, familyName, Lopukhina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lopukhina Context triple: [Eudoxia Lopukhina, familyName, Lopukhina]
-
A.
Kashirina
Kashirina is a Russian surname most notably borne by Varvara Vasilyevna Kashirina.
-
B.
Kuntsevskaya
Kuntsevskaya is a Moscow Metro station on the Big Circle Line serving the Kuntsevo District in western Moscow.
-
C.
Shubskaya
Shubskaya is a Russian surname most notably associated with Anastasia Shubskaya, a film producer and the wife of hockey star Alexander Ovechkin.
-
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: Lopukhina Triple: [Eudoxia Lopukhina, familyName, Lopukhina]
Generated description
Lopukhina is a Russian noble family name historically associated with Eudoxia Lopukhina, the first wife of Tsar Peter the Great.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lopukhina Target entity description: Lopukhina is a Russian noble family name historically associated with Eudoxia Lopukhina, the first wife of Tsar Peter the Great.
-
A.
Kashirina
Kashirina is a Russian surname most notably borne by Varvara Vasilyevna Kashirina.
-
B.
Kuntsevskaya
Kuntsevskaya is a Moscow Metro station on the Big Circle Line serving the Kuntsevo District in western Moscow.
-
C.
Shubskaya
Shubskaya is a Russian surname most notably associated with Anastasia Shubskaya, a film producer and the wife of hockey star Alexander Ovechkin.
-
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_69ab49dcee188190b5c6eca9ae9e3469 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde2ec2ac8190bd702ad3eafb6aed |
completed | March 7, 2026, 8:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afce94a8f48190ac8b447f66b7d545 |
completed | March 10, 2026, 7:56 a.m. |
| NEDg | Description generation | batch_69afcf3aa64081909fe4007d94df48c2 |
completed | March 10, 2026, 7:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afcfe8a140819095daa37d539e4c72 |
completed | March 10, 2026, 8:01 a.m. |
Created at: March 6, 2026, 9:59 p.m.