Triple
T6853099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Western Siberia |
E158069
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Tyumen |
E232706
|
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: Tyumen | Statement: [Western Siberia, contains, Tyumen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tyumen Context triple: [Western Siberia, contains, Tyumen]
-
A.
Tyumen
chosen
Tyumen is a historic city in western Siberia, Russia, known as an early Russian settlement in Siberia and now a major industrial and administrative center.
-
B.
Nizhnevartovsk
Nizhnevartovsk is a major oil-producing city in western Siberia, Russia, known as one of the centers of the country’s petroleum industry.
-
C.
Krasnoyarsk
Krasnoyarsk is a large industrial and cultural city in central Russia, situated on the Yenisei River and known as one of the key urban centers of Siberia.
-
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.
Irkutsk
Irkutsk is a major city in southeastern Siberia, Russia, historically significant as a political and administrative center and a key hub during the Russian Civil War.
- 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_69c6882fae988190864cbba788c5ebb4 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d84fffbc8190943ca7f3f03937e9 |
completed | March 27, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c81e9cc3b88190a96ced453be3691e |
completed | March 28, 2026, 6:31 p.m. |
Created at: March 27, 2026, 2:20 p.m.