Triple
T14090882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lower Volga |
E339126
|
entity |
| Predicate | majorCityOnRegion |
P50537
|
FINISHED |
| Object | Kamyshin |
E237038
|
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: Kamyshin | Statement: [Lower Volga, majorCityOnRegion, Kamyshin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kamyshin Context triple: [Lower Volga, majorCityOnRegion, Kamyshin]
-
A.
Kamyshin
chosen
Kamyshin is a significant industrial and river port city on the Volga River in southwestern Russia.
-
B.
Zaraysk
Zaraysk is a historic town in Moscow Oblast, Russia, known for its well-preserved medieval kremlin and role as a former regional administrative center.
-
C.
Kalyazin
Kalyazin is a historic town in Tver Oblast, Russia, known for its partially submerged bell tower in the Uglich Reservoir.
-
D.
Yelabuga
Yelabuga is a historic town in the Republic of Tatarstan, Russia, known for its preserved merchant architecture and cultural heritage.
-
E.
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.
- 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_69d81c687b0c819087fd9ed4198403f8 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5ee3213c8190af2853a2a5b302a2 |
completed | April 14, 2026, 3:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd6476c15881909881b9f85697a9b2 |
completed | May 8, 2026, 4:20 a.m. |
Created at: April 9, 2026, 10:21 p.m.