Triple
T19280683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belogorsk |
E482178
|
entity |
| Predicate | hasFormerName |
P65
|
FINISHED |
| Object | Kuznetsovo |
—
|
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: Kuznetsovo | Statement: [Belogorsk, hasFormerName, Kuznetsovo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kuznetsovo Context triple: [Belogorsk, hasFormerName, Kuznetsovo]
-
A.
Kuznetsovo
chosen
Kuznetsovo is the former name of the town now known as Belogorsk in Russia’s Amur Oblast.
-
B.
Zayukovo
Zayukovo is a rural locality in the Kabardino-Balkar Republic of Russia situated along the Baksan River in the North Caucasus region.
-
C.
Kuvshinovo
Kuvshinovo is a small town in Tver Oblast, Russia, known primarily as a local industrial and administrative center.
-
D.
Yuzovka
Yuzovka was the original name of the industrial settlement in eastern Ukraine that later developed into the city of Donetsk.
-
E.
Rumyantsevo
Rumyantsevo is a Moscow Metro station serving the southwestern part of the city near the Troparevo-Nikulino area.
- 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_69d8e8cf61b0819096fe3e4107827c4e |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fbfdfaf481909e0434f33053cc62 |
completed | April 20, 2026, 10:12 a.m. |
Created at: April 10, 2026, 1:30 p.m.