Triple
T10875949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bashkir ASSR |
E256797
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
Neftekamsk
Neftekamsk is an industrial city in the Republic of Bashkortostan, Russia, known for its oil-related industries and vehicle manufacturing.
|
E901846
|
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: Neftekamsk | Statement: [Bashkir ASSR, hasMajorCity, Neftekamsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Neftekamsk Context triple: [Bashkir ASSR, hasMajorCity, Neftekamsk]
-
A.
Nizhnekamsk
Nizhnekamsk is a major industrial city in Russia known for its large petrochemical and oil refining complexes.
-
B.
Monchegorsk
Monchegorsk is an industrial town in Russia’s Murmansk Oblast known for its large nickel and copper smelting operations within the Arctic Kola Peninsula region.
-
C.
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.
-
D.
Novokuznetskaya
Novokuznetskaya is a Moscow Metro station known for its distinctive Stalinist architecture and richly decorated interiors.
-
E.
Volokolamsk
Volokolamsk is a historic town in western Russia, located northwest of Moscow and known for its medieval origins and role in regional trade and defense.
- 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: Neftekamsk Triple: [Bashkir ASSR, hasMajorCity, Neftekamsk]
Generated description
Neftekamsk is an industrial city in the Republic of Bashkortostan, Russia, known for its oil-related industries and vehicle manufacturing.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Neftekamsk Target entity description: Neftekamsk is an industrial city in the Republic of Bashkortostan, Russia, known for its oil-related industries and vehicle manufacturing.
-
A.
Nizhnekamsk
Nizhnekamsk is a major industrial city in Russia known for its large petrochemical and oil refining complexes.
-
B.
Monchegorsk
Monchegorsk is an industrial town in Russia’s Murmansk Oblast known for its large nickel and copper smelting operations within the Arctic Kola Peninsula region.
-
C.
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.
-
D.
Novokuznetskaya
Novokuznetskaya is a Moscow Metro station known for its distinctive Stalinist architecture and richly decorated interiors.
-
E.
Volokolamsk
Volokolamsk is a historic town in western Russia, located northwest of Moscow and known for its medieval origins and role in regional trade and defense.
- 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_69d6aa848804819081b2713ca0bedf06 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d751ac901881909938cabe4d21bdbf |
completed | April 9, 2026, 7:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3a91a3e1c819083ef144e7fd5603f |
completed | April 18, 2026, 3:54 p.m. |
| NEDg | Description generation | batch_69e3ad00b5c08190a7bf3ecbeae76d88 |
completed | April 18, 2026, 4:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3b1efe4a88190884eb5186954cf39 |
completed | April 18, 2026, 4:31 p.m. |
Created at: April 8, 2026, 9:21 p.m.