Triple
T11459567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yoshkar-Ola |
E271616
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object |
Krasnokokshaysk
Krasnokokshaysk is the former name of the city now known as Yoshkar-Ola, the capital of the Mari El Republic in Russia.
|
E928349
|
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: Krasnokokshaysk | Statement: [Yoshkar-Ola, formerName, Krasnokokshaysk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Krasnokokshaysk Context triple: [Yoshkar-Ola, formerName, Krasnokokshaysk]
-
A.
Krasnoturyinsk
Krasnoturyinsk is an industrial town in Russia’s Ural region known for its mining and metallurgical industries.
-
B.
Kuvshinovo
Kuvshinovo is a small town in Tver Oblast, Russia, known primarily as a local industrial and administrative center.
-
C.
Kamyshlov
Kamyshlov is a small historic town in Russia’s Ural region, known for its traditional wooden architecture and role as a local administrative and cultural center.
-
D.
Makeyevka
Makeyevka is an industrial city in eastern Ukraine’s Donetsk Oblast, historically known for its coal mining and metallurgical industries.
-
E.
Konakovo
Konakovo is a town in Tver Oblast, Russia, situated on the Volga River and known for its power station and riverside recreation.
- 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: Krasnokokshaysk Triple: [Yoshkar-Ola, formerName, Krasnokokshaysk]
Generated description
Krasnokokshaysk is the former name of the city now known as Yoshkar-Ola, the capital of the Mari El Republic in Russia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Krasnokokshaysk Target entity description: Krasnokokshaysk is the former name of the city now known as Yoshkar-Ola, the capital of the Mari El Republic in Russia.
-
A.
Krasnoturyinsk
Krasnoturyinsk is an industrial town in Russia’s Ural region known for its mining and metallurgical industries.
-
B.
Kuvshinovo
Kuvshinovo is a small town in Tver Oblast, Russia, known primarily as a local industrial and administrative center.
-
C.
Kamyshlov
Kamyshlov is a small historic town in Russia’s Ural region, known for its traditional wooden architecture and role as a local administrative and cultural center.
-
D.
Makeyevka
Makeyevka is an industrial city in eastern Ukraine’s Donetsk Oblast, historically known for its coal mining and metallurgical industries.
-
E.
Konakovo
Konakovo is a town in Tver Oblast, Russia, situated on the Volga River and known for its power station and riverside recreation.
- 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_69d6aadff8888190a13f253f0d460874 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f2138081909408c7916cef99c9 |
completed | April 9, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e6040733648190a10f9553b3ac87a7 |
completed | April 20, 2026, 10:46 a.m. |
| NEDg | Description generation | batch_69e610a07bf881908de79850edb9576f |
completed | April 20, 2026, 11:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e617fdaaa88190a1860fb00309596b |
completed | April 20, 2026, 12:11 p.m. |
Created at: April 8, 2026, 9:35 p.m.