Triple
T17003535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kursky railway terminal |
E412508
|
entity |
| Predicate | connectsToCity |
P4245
|
FINISHED |
| Object | Voronezh |
—
|
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: Voronezh | Statement: [Kursky railway terminal, connectsToCity, Voronezh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Voronezh Context triple: [Kursky railway terminal, connectsToCity, Voronezh]
-
A.
Voronezh
chosen
Voronezh is a major city in southwestern Russia, situated on the Voronezh River and serving as an important cultural, industrial, and transportation center.
-
B.
Rostov
Rostov is one of Russia’s oldest and most historically significant towns, renowned for its well-preserved kremlin and traditional architecture.
-
C.
Belgorod
Belgorod is a city in western Russia near the Ukrainian border, historically significant as a strategic site of major World War II battles and offensives.
-
D.
Rostov-on-Don
Rostov-on-Don is a major port city in southern Russia, located on the Don River near the Sea of Azov and serving as an important administrative, cultural, and industrial center of the region.
-
E.
Ryazhsk
Ryazhsk is a historic town in Ryazan Oblast, Russia, known as a former local administrative center dating back to the Russian Empire.
- 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_69d886cb581c8190ab05f4b429c9cd85 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d380f968819097b59e7bac333ea4 |
completed | April 18, 2026, 6:54 p.m. |
Created at: April 10, 2026, 5:32 a.m.