Triple
T5604606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leningradsky Prospekt |
E147201
|
entity |
| Predicate | hasNearbyMetroStation |
P26735
|
FINISHED |
| Object | Sokol |
E222042
|
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: Sokol | Statement: [Leningradsky Prospekt, hasNearbyMetroStation, Sokol]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sokol Context triple: [Leningradsky Prospekt, hasNearbyMetroStation, Sokol]
-
A.
Sokol
chosen
Sokol is a Moscow Metro station on the Zamoskvoretskaya Line, serving the Sokol District in the north of the city.
-
B.
Sokol
Sokol is a town in Russia known as an industrial and administrative center within Vologda Oblast.
-
C.
Sokol Kiev
Sokol Kiev was an ice hockey club from Kyiv that competed at the top level of Soviet hockey in the Soviet Championship League.
-
D.
Sokolka
Sokolka is a town in present-day northeastern Poland, historically part of the Grodno region, known for its multicultural heritage and role as a local administrative and trade center.
-
E.
Atleti
Atleti is the commonly used nickname for Atlético de Madrid, a major Spanish professional football club based in Madrid.
- 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_69c0090500f881908374285baf0ac46f |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020f9408481908cf006074c726301 |
completed | March 22, 2026, 5:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0287649cc8190ae356790dd993973 |
completed | March 22, 2026, 5:35 p.m. |
Created at: March 22, 2026, 3:39 p.m.