Triple
T7320910
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sverdlovsk Oblast |
E168541
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Nizhny Tagil |
E49265
|
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: Nizhny Tagil | Statement: [Sverdlovsk Oblast, hasCity, Nizhny Tagil]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nizhny Tagil Context triple: [Sverdlovsk Oblast, hasCity, Nizhny Tagil]
-
A.
Nizhny Tagil
chosen
Nizhny Tagil is a major industrial city in Russia’s Sverdlovsk Oblast, historically known for its metallurgical plants and role in the country’s heavy industry.
-
B.
Magnitogorsk
Magnitogorsk is a major industrial city in Russia’s Chelyabinsk Oblast, historically centered around one of the world’s largest iron and steel works.
-
C.
Sverdlovsk
Sverdlovsk is the former name of Yekaterinburg, a major industrial and cultural city in Russia’s Ural region.
-
D.
Kuznetsk
Kuznetsk is a city in Penza Oblast, Russia, known as an industrial and transport center in the Volga region.
-
E.
Nizhnekamsk
Nizhnekamsk is a major industrial city in Russia known for its large petrochemical and oil refining complexes.
- 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_69c68a5251508190ad68df4151cfeb04 |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6ef1ba58481909cfb5030b85f385a |
completed | March 27, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8c7adaf3c819095214864b91316f6 |
completed | March 29, 2026, 6:33 a.m. |
Created at: March 27, 2026, 3:02 p.m.