Triple
T20303137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kuznetsovo |
E505535
|
entity |
| Predicate | nowKnownAs |
P22915
|
FINISHED |
| Object | Belogorsk |
—
|
NE NERFINISHED |
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: Belogorsk | Statement: [Kuznetsovo, nowKnownAs, Belogorsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belogorsk Context triple: [Kuznetsovo, nowKnownAs, Belogorsk]
-
A.
Belogorsk
chosen
Belogorsk is a city in Russia’s Far East that serves as an important regional center within Amur Oblast.
-
B.
Bolkhov
Bolkhov is a historic town in western Russia known for its old churches and traditional architecture within Oryol Oblast.
-
C.
Borisoglebsk
Borisoglebsk is a small Russian city known for its historical architecture and location on the Vorona River in southwestern Russia.
-
D.
Belozersk
Belozersk is a historic town in northwestern Russia known for its medieval heritage and location near Lake Beloye.
-
E.
Belorechensk
Belorechensk is a town in Russia’s Krasnodar Krai known for its industrial enterprises and location near the Belaya River in the North Caucasus region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4b8ab648190906e18538c250148 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6773e0864819095d272659cd2074d |
completed | April 20, 2026, 6:58 p.m. |
Created at: April 16, 2026, 11:17 a.m.