Triple
T15300194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Novosibirsk Metro |
E365764
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Studencheskaya
Studencheskaya is a metro station in the Novosibirsk Metro system in Novosibirsk, Russia.
|
E1149212
|
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: Studencheskaya | Statement: [Novosibirsk Metro, hasStation, Studencheskaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Studencheskaya Context triple: [Novosibirsk Metro, hasStation, Studencheskaya]
-
A.
Studencheskaya
Studencheskaya is a Moscow Metro station located near Kutuzovsky Prospekt in Moscow, Russia.
-
B.
Бауманская
Бауманская — станция Московского метрополитена, расположенная в центральной части города и обслуживающая исторический район с развитой городской инфраструктурой.
-
C.
Karazin
Karazin is a Slavic surname most notably associated with Vasiliy Karazin, a prominent Ukrainian educator and founder of Kharkiv University.
-
D.
Yuriev University
Yuriev University was a historical Russian higher education institution that served as a predecessor to Voronezh State University.
-
E.
Akademichesky District
Akademichesky District is a residential and educational neighborhood in Moscow, Russia, known for its scientific institutions and proximity to major transport routes.
- 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: Studencheskaya Triple: [Novosibirsk Metro, hasStation, Studencheskaya]
Generated description
Studencheskaya is a metro station in the Novosibirsk Metro system in Novosibirsk, Russia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Studencheskaya Target entity description: Studencheskaya is a metro station in the Novosibirsk Metro system in Novosibirsk, Russia.
-
A.
Studencheskaya
Studencheskaya is a Moscow Metro station located near Kutuzovsky Prospekt in Moscow, Russia.
-
B.
Бауманская
Бауманская — станция Московского метрополитена, расположенная в центральной части города и обслуживающая исторический район с развитой городской инфраструктурой.
-
C.
Karazin
Karazin is a Slavic surname most notably associated with Vasiliy Karazin, a prominent Ukrainian educator and founder of Kharkiv University.
-
D.
Yuriev University
Yuriev University was a historical Russian higher education institution that served as a predecessor to Voronezh State University.
-
E.
Akademichesky District
Akademichesky District is a residential and educational neighborhood in Moscow, Russia, known for its scientific institutions and proximity to major transport routes.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0368869f8819098cf9e7801e37548 |
completed | April 16, 2026, 1:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feef8513a08190b2d2a7dde85dd43d |
completed | May 9, 2026, 8:25 a.m. |
| NEDg | Description generation | batch_69fef23de4688190beeb59ef43891e3d |
completed | May 9, 2026, 8:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fef2d8fe04819084bb3deb6859d746 |
completed | May 9, 2026, 8:39 a.m. |
Created at: April 10, 2026, 3:15 a.m.