Triple
T7320911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sverdlovsk Oblast |
E168541
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Kamensk-Uralsky
Kamensk-Uralsky is an industrial city in Russia’s Ural region, known for its metallurgical plants and strategic location on the Iset River.
|
E688435
|
NE FINISHED |
How this triple was built (4 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: Kamensk-Uralsky | Statement: [Sverdlovsk Oblast, hasCity, Kamensk-Uralsky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kamensk-Uralsky Context triple: [Sverdlovsk Oblast, hasCity, Kamensk-Uralsky]
-
A.
Kamyensk-Shakhtinsky
Kamyensk-Shakhtinsky is a city in southwestern Russia known as an industrial and transport center within Rostov Oblast.
-
B.
Nizhnekamsk
Nizhnekamsk is a major industrial city in Russia known for its large petrochemical and oil refining complexes.
-
C.
Kuznetsk
Kuznetsk is a city in Penza Oblast, Russia, known as an industrial and transport center in the Volga region.
-
D.
Kirovsk
Kirovsk is an industrial town in Russia’s Murmansk Oblast, known for its mining industry and location in the Khibiny Mountains on the Kola Peninsula.
-
E.
Kirovsk
Kirovsk is a small industrial town in northwestern Russia, situated near Saint Petersburg along the Neva River.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kamensk-Uralsky Triple: [Sverdlovsk Oblast, hasCity, Kamensk-Uralsky]
Generated description
Kamensk-Uralsky is an industrial city in Russia’s Ural region, known for its metallurgical plants and strategic location on the Iset River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kamensk-Uralsky Target entity description: Kamensk-Uralsky is an industrial city in Russia’s Ural region, known for its metallurgical plants and strategic location on the Iset River.
-
A.
Kamyensk-Shakhtinsky
Kamyensk-Shakhtinsky is a city in southwestern Russia known as an industrial and transport center within Rostov Oblast.
-
B.
Nizhnekamsk
Nizhnekamsk is a major industrial city in Russia known for its large petrochemical and oil refining complexes.
-
C.
Kuznetsk
Kuznetsk is a city in Penza Oblast, Russia, known as an industrial and transport center in the Volga region.
-
D.
Kirovsk
Kirovsk is a small industrial town in northwestern Russia, situated near Saint Petersburg along the Neva River.
-
E.
Kirovsk
Kirovsk is an industrial town in Russia’s Murmansk Oblast, known for its mining industry and location in the Khibiny Mountains on the Kola Peninsula.
- F. None of above. chosen
Provenance (5 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_69c8d68a7e908190831b9f7f84ef19bd |
completed | March 29, 2026, 7:36 a.m. |
| NEDg | Description generation | batch_69c8daa512c881909a657ed147969224 |
completed | March 29, 2026, 7:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8daf11044819084f60c82e4d746f2 |
completed | March 29, 2026, 7:55 a.m. |
Created at: March 27, 2026, 3:02 p.m.