Triple
T9207375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vyatka Governorate |
E221016
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Vyatka |
E144836
|
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: Vyatka | Statement: [Vyatka Governorate, capital, Vyatka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vyatka Context triple: [Vyatka Governorate, capital, 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.
Yuryatin
Yuryatin is a fictional Russian town in Boris Pasternak’s novel "Doctor Zhivago," serving as a key setting in Lara Antipova’s story.
-
C.
Vyazemsky
Vyazemsky is a small town in Russia’s Far Eastern Federal District, serving as an administrative center within Khabarovsk Krai.
-
D.
Volzhsky
Volzhsky is a major industrial city in southwestern Russia located across the Volga River from Volgograd.
-
E.
Grusinskaya
Grusinskaya is a fading but still celebrated Russian ballerina whose loneliness and vulnerability are central to the drama of the film "Grand Hotel."
- 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_69ca83e9d0e081908bdb71097201a06c |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd9b0b6788190908bee67a0c5d48f |
completed | April 1, 2026, 8:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0b1adc2508190b8a24510ee61f092 |
completed | April 4, 2026, 6:37 a.m. |
Created at: March 30, 2026, 7:26 p.m.