Triple
T2581688
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tver Oblast |
E57105
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Vyshny Volochyok |
E63408
|
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: Vyshny Volochyok | Statement: [Tver Oblast, contains, Vyshny Volochyok]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vyshny Volochyok Context triple: [Tver Oblast, contains, Vyshny Volochyok]
-
A.
Vyshny Volochyok
chosen
Vyshny Volochyok is a historic town in Tver Oblast, Russia, known as a former key transport hub on the waterway between Moscow and Saint Petersburg.
-
B.
Sorochyntsi
Sorochyntsi is a village in central Ukraine best known as the birthplace of writer Nikolai Gogol and for its traditional fairs and cultural heritage.
-
C.
Medveditsa
Medveditsa is a river in southwestern Russia that flows through the Volgograd and Saratov regions before joining the Don River.
-
D.
Kakhovskaya
Kakhovskaya is a Moscow Metro station that serves as part of the city’s Big Circle Line.
-
E.
Makiyivka
Makiyivka is a major industrial city in eastern Ukraine’s Donetsk region, historically known for its coal mining and heavy industry.
- 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_69ab4a4dca6481908c301f8e317396e7 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd3c843bc8190837cea3441bf3ca1 |
completed | March 7, 2026, 7:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af657cc2b08190a9055d6da7744851 |
completed | March 10, 2026, 12:27 a.m. |
Created at: March 6, 2026, 9:49 p.m.