Triple
T5604604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leningradsky Prospekt |
E147201
|
entity |
| Predicate | hasNearbyMetroStation |
P26735
|
FINISHED |
| Object | Dinamo |
E225077
|
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: Dinamo | Statement: [Leningradsky Prospekt, hasNearbyMetroStation, Dinamo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dinamo Context triple: [Leningradsky Prospekt, hasNearbyMetroStation, Dinamo]
-
A.
Dinamo
chosen
Dinamo is a Moscow Metro station named after the nearby Dynamo sports complex and stadium, serving passengers on the Zamoskvoretskaya Line.
-
B.
Dinamo Riga
Dinamo Riga is a professional ice hockey club based in Riga, Latvia, known for competing in top European and international leagues.
-
C.
Dynamo Moscow
Dynamo Moscow is a prominent Russian professional ice hockey club based in Moscow, historically known for developing elite players such as Alex Ovechkin.
-
D.
Dynamo Minsk
Dynamo Minsk is a professional sports club from Minsk, Belarus, best known for its football team competing in the country’s top league and European competitions.
-
E.
Dynamo Bucharest
Dynamo Bucharest is a prominent Romanian sports club best known for its historically successful football team based in Bucharest.
- 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.