Triple
T17480894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Savyolovsky railway terminal |
E425654
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Dubna |
—
|
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: Dubna | Statement: [Savyolovsky railway terminal, connectsTo, Dubna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dubna Context triple: [Savyolovsky railway terminal, connectsTo, Dubna]
-
A.
Dubna
chosen
Dubna is a Russian town in the Moscow Oblast best known as a major center for nuclear research and home to the Joint Institute for Nuclear Research.
-
B.
Obninsk
Obninsk is a Russian city best known as the site of the world’s first grid-connected nuclear power plant and an important center for nuclear and scientific research.
-
C.
Dubna Urban Okrug
Dubna Urban Okrug is a municipal formation in Moscow Oblast, Russia, that encompasses the science city of Dubna and its surrounding territory under a unified local government.
-
D.
Podolsk
Podolsk is a major industrial city and former center of machine-building located just south of Moscow in western Russia.
-
E.
Kolpino
Kolpino is a town in the Kolpinsky District of Saint Petersburg, Russia, known as an industrial suburb with significant metallurgical and manufacturing enterprises.
- 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_69d889dccf7481909264a1844a2e9100 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e451bfd75481908c20bc2c1cbff593 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 5:48 a.m.