Triple
T10176402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kuvshinovsky District |
E235862
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Kuvshinovo |
E874683
|
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: Kuvshinovo | Statement: [Kuvshinovsky District, hasSettlement, Kuvshinovo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kuvshinovo Context triple: [Kuvshinovsky District, hasSettlement, Kuvshinovo]
-
A.
Kuvshinovo
chosen
Kuvshinovo is a small town in Tver Oblast, Russia, known primarily as a local industrial and administrative center.
-
B.
Kamyshlov
Kamyshlov is a small historic town in Russia’s Ural region, known for its traditional wooden architecture and role as a local administrative and cultural center.
-
C.
Konakovo
Konakovo is a town in Tver Oblast, Russia, situated on the Volga River and known for its power station and riverside recreation.
-
D.
Makeyevka
Makeyevka is an industrial city in eastern Ukraine’s Donetsk Oblast, historically known for its coal mining and metallurgical industries.
-
E.
Yalutorovsk
Yalutorovsk is a historic town in western Siberia, Russia, known for its 17th-century origins as a fortress settlement and its location on the Tobol River.
- 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_69ca84d1d5f88190ab878a1021ecff68 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdecd3a2688190bce277bffffcbf8b |
completed | April 2, 2026, 4:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d96b0e4eac8190af28db3d334852cb |
completed | April 10, 2026, 9:26 p.m. |
Created at: March 30, 2026, 9:11 p.m.