Triple
T19806936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kirov Oblast |
E475836
|
entity |
| Predicate | historicalRegion |
P915
|
FINISHED |
| Object | Vyatka |
—
|
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: Vyatka | Statement: [Kirov Oblast, historicalRegion, Vyatka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vyatka Context triple: [Kirov Oblast, historicalRegion, Vyatka]
-
A.
Vyatka
chosen
Vyatka was a historic region and town in northeastern European Russia, known as a frontier area that was gradually incorporated into the centralized Russian state.
-
B.
Tikhon
Tikhon was the religious name of Patriarch Tikhon of Moscow, the early 20th-century head of the Russian Orthodox Church known for leading it through the turmoil of the Russian Revolution and early Soviet period.
-
C.
Yuryatin
Yuryatin is a fictional Russian town in Boris Pasternak’s novel "Doctor Zhivago," serving as a key setting in Lara Antipova’s story.
-
D.
Vyazemsky
Vyazemsky is a small town in Russia’s Far Eastern Federal District, serving as an administrative center within Khabarovsk Krai.
-
E.
Yurka
Yurka is the surname of Blanche Yurka, an American actress and director known for her work on stage and in early cinema.
- 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_69d8e51bc4208190a1c57d8c5d1b15e4 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65428f5c48190be6ae0d6a77675d2 |
completed | April 20, 2026, 4:28 p.m. |
Created at: April 10, 2026, 1:49 p.m.