Triple
T10192034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Novogireyevo |
E238058
|
entity |
| Predicate | hasArchitect |
P184
|
FINISHED |
| Object |
N. Shurygina
N. Shurygina is an architect known for contributing to the design and development of the Novogireyevo district in Moscow.
|
E847239
|
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: N. Shurygina | Statement: [Novogireyevo, hasArchitect, N. Shurygina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: N. Shurygina Context triple: [Novogireyevo, hasArchitect, N. Shurygina]
-
A.
Marfa Lapkina
Marfa Lapkina was a Soviet actress best known for her leading role in Sergei Eisenstein’s silent film "The General Line" (also known as "Old and New").
-
B.
Lilia Podkopayeva
Lilia Podkopayeva is a Ukrainian artistic gymnast and 1996 Olympic all-around champion renowned for her elegant style and highly difficult routines.
-
C.
Olga Belokopytova
Olga Belokopytova was the wife of Nobel Prize–winning Russian biologist and immunologist Ilya Ilyich Mechnikov.
-
D.
Olga Naumova
Olga Naumova was the wife of renowned Russian-born conductor and double-bassist Serge Koussevitzky, accompanying him through the early part of his musical career.
-
E.
Nina Grebeshkova
Nina Grebeshkova is a Soviet and Russian film and theater actress best known for her roles in classic comedies of the 1960s and 1970s.
- 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: N. Shurygina Triple: [Novogireyevo, hasArchitect, N. Shurygina]
Generated description
N. Shurygina is an architect known for contributing to the design and development of the Novogireyevo district in Moscow.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: N. Shurygina Target entity description: N. Shurygina is an architect known for contributing to the design and development of the Novogireyevo district in Moscow.
-
A.
Marfa Lapkina
Marfa Lapkina was a Soviet actress best known for her leading role in Sergei Eisenstein’s silent film "The General Line" (also known as "Old and New").
-
B.
Lilia Podkopayeva
Lilia Podkopayeva is a Ukrainian artistic gymnast and 1996 Olympic all-around champion renowned for her elegant style and highly difficult routines.
-
C.
Olga Belokopytova
Olga Belokopytova was the wife of Nobel Prize–winning Russian biologist and immunologist Ilya Ilyich Mechnikov.
-
D.
Olga Naumova
Olga Naumova was the wife of renowned Russian-born conductor and double-bassist Serge Koussevitzky, accompanying him through the early part of his musical career.
-
E.
Nina Grebeshkova
Nina Grebeshkova is a Soviet and Russian film and theater actress best known for her roles in classic comedies of the 1960s and 1970s.
- 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_69ca84de1b208190bf17bb305b002605 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdedc4fb808190aae2e4b84be96f83 |
completed | April 2, 2026, 4:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d317ca2cf481909cf715ef9248be3c |
completed | April 6, 2026, 2:17 a.m. |
| NEDg | Description generation | batch_69d3188886908190ba0a5539ce942980 |
completed | April 6, 2026, 2:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d31c4fb8288190bbc6b3d4a79dafb1 |
completed | April 6, 2026, 2:37 a.m. |
Created at: March 30, 2026, 9:13 p.m.