Triple
T1855031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yamal Airlines |
E41681
|
entity |
| Predicate | operatesTo |
P6304
|
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: [Yamal Airlines, operatesTo, Tyumen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tyumen Context triple: [Yamal Airlines, operatesTo, 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.
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.
-
C.
Yakutsk
Yakutsk is a major city in northeastern Siberia, Russia, known as one of the coldest large cities in the world and a key administrative and cultural center of the Sakha Republic.
-
D.
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.
-
E.
Tomsk
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.
- 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_69a8864a83848190a4ec02721306c511 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb7c2354081909ee4da7669932796 |
completed | March 7, 2026, 5:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af2b4b974081908da05bc63f923215 |
completed | March 9, 2026, 8:19 p.m. |
Created at: March 4, 2026, 7:33 p.m.