Triple
T10689662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kuznetsky Most |
E251975
|
entity |
| Predicate | architect |
P184
|
FINISHED |
| Object | N. Shurygina |
E847239
|
NE FINISHED |
How this triple was built (2 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: [Kuznetsky Most, architect, N. Shurygina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: N. Shurygina Context triple: [Kuznetsky Most, architect, N. Shurygina]
-
A.
N. Shurygina
chosen
N. Shurygina is an architect known for contributing to the design and development of the Novogireyevo district in Moscow.
-
B.
N. Samoylova
N. Samoylova is an architect known for work on projects along Moscow’s historic Kuznetsky Most street.
-
C.
Yu. Kolesnikova
Yu. Kolesnikova is an architect known for designing the Bagrationovskaya station in the Moscow Metro system.
-
D.
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").
-
E.
Lilia Podkopayeva
Lilia Podkopayeva is a Ukrainian artistic gymnast and 1996 Olympic all-around champion renowned for her elegant style and highly difficult routines.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6aa5bd7c08190a816e733b4045c23 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fd1c0f0081908a6869ee756ec789 |
completed | April 9, 2026, 1:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d998cfeb748190bfff06534d83cf97 |
completed | April 11, 2026, 12:41 a.m. |
Created at: April 8, 2026, 9:11 p.m.