Triple
T15300149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Novosibirsk-Glavny |
E365763
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Tomsk |
E208233
|
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: Tomsk | Statement: [Novosibirsk-Glavny, connectsTo, Tomsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tomsk Context triple: [Novosibirsk-Glavny, connectsTo, Tomsk]
-
A.
Tomsk
chosen
Tomsk is a historic university and research city in southwestern Siberia, known as one of the region’s oldest and most important cultural and educational centers.
-
B.
Omsk
Omsk is one of the largest cities in southwestern Siberia, Russia, serving as a major industrial, cultural, and transportation hub on the Irtysh River.
-
C.
Novosibirsk
Novosibirsk is a major city in southwestern Siberia and the third-largest city in Russia, known as an important industrial, scientific, and cultural center.
-
D.
Tobolsk
Tobolsk is a historic Siberian town in Russia known for its Kremlin and as a place of exile and imprisonment during the late imperial period.
-
E.
Neftekamsk
Neftekamsk is an industrial city in the Republic of Bashkortostan, Russia, known for its oil-related industries and vehicle manufacturing.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0368869f8819098cf9e7801e37548 |
completed | April 16, 2026, 1:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d3cf84c8190a4655803b12c9721 |
completed | May 9, 2026, 1:57 p.m. |
Created at: April 10, 2026, 3:15 a.m.