Triple
T6769446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint Petersburg Metro |
E155005
|
entity |
| Predicate | notableStation |
P3858
|
FINISHED |
| Object |
Narvskaya
Narvskaya is a station on the Saint Petersburg Metro, known for its Stalinist architecture and historical Soviet-themed design.
|
E619861
|
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: Narvskaya | Statement: [Saint Petersburg Metro, notableStation, Narvskaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Narvskaya Context triple: [Saint Petersburg Metro, notableStation, Narvskaya]
-
A.
Voykovskaya
Voykovskaya is a Moscow Metro station serving the Zamoskvoretskaya Line in the northern part of the city.
-
B.
Nagatinskaya
Nagatinskaya is a Moscow Metro station serving the Serpukhovsko–Timiryazevskaya Line in the Nagatinsky Zaton area of southern Moscow.
-
C.
Mishaninskaya
Mishaninskaya is a rural locality in Russia best known as the birthplace of the polymath and scientist Mikhail Lomonosov.
-
D.
Noyabrsk
Noyabrsk is a major oil and gas industry city in northern Russia, located in the Yamalo-Nenets region of Western Siberia.
-
E.
Savyolovskaya
Savyolovskaya is a Moscow Metro station on the Big Circle Line, serving as part of the city’s modern orbital rapid transit 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: Narvskaya Triple: [Saint Petersburg Metro, notableStation, Narvskaya]
Generated description
Narvskaya is a station on the Saint Petersburg Metro, known for its Stalinist architecture and historical Soviet-themed design.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Narvskaya Target entity description: Narvskaya is a station on the Saint Petersburg Metro, known for its Stalinist architecture and historical Soviet-themed design.
-
A.
Voykovskaya
Voykovskaya is a Moscow Metro station serving the Zamoskvoretskaya Line in the northern part of the city.
-
B.
Nagatinskaya
Nagatinskaya is a Moscow Metro station serving the Serpukhovsko–Timiryazevskaya Line in the Nagatinsky Zaton area of southern Moscow.
-
C.
Mishaninskaya
Mishaninskaya is a rural locality in Russia best known as the birthplace of the polymath and scientist Mikhail Lomonosov.
-
D.
Noyabrsk
Noyabrsk is a major oil and gas industry city in northern Russia, located in the Yamalo-Nenets region of Western Siberia.
-
E.
Savyolovskaya
Savyolovskaya is a Moscow Metro station on the Big Circle Line, serving as part of the city’s modern orbital rapid transit 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_69c68812ef7c819099369f51febb725c |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d232d1f08190bc30c0f24f28c475 |
completed | March 27, 2026, 6:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c71a7da01c8190995885eeb4ba6253 |
completed | March 28, 2026, 12:02 a.m. |
| NEDg | Description generation | batch_69c71b88b27c8190b803f0e9f6402c44 |
completed | March 28, 2026, 12:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c71c91e08c81908be81efc2087464a |
completed | March 28, 2026, 12:10 a.m. |
Created at: March 27, 2026, 2:12 p.m.