Triple
T15505185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Onega River |
E379061
|
entity |
| Predicate | RussianName |
P744
|
FINISHED |
| Object | Онега |
E1010996
|
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: Онега | Statement: [Onega River, RussianName, Онега]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Онега Context triple: [Onega River, RussianName, Онега]
-
A.
Onega
chosen
Onega is a small town in Arkhangelsk Oblast, northwestern Russia, situated near the mouth of the Onega River on the White Sea coast.
-
B.
Novorybnaya
Novorybnaya is a small rural settlement located along the Khatanga River in the remote Arctic region of northern Siberia, Russia.
-
C.
Krasnoufimsk
Krasnoufimsk is a small historic town in Russia’s Ural region, known for its traditional architecture and role as a local administrative and cultural center.
-
D.
Kholmogory
Kholmogory is a historic Russian town in the Arkhangelsk region that served as an important early northern trading and administrative center.
-
E.
Snezhnaya
Snezhnaya is a river in Siberia, Russia, known for flowing through remote mountainous terrain before joining Lake Baikal.
- 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_69d85cd53a7c819080f5b9042c4c199e |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03fcd5d948190b25a67a72ef980e9 |
completed | April 16, 2026, 1:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff454ca0f0819088ba846a448dda2e |
completed | May 9, 2026, 2:31 p.m. |
Created at: April 10, 2026, 3:55 a.m.