Triple
T4891131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tula Oblast |
E109563
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
Aleksin
Aleksin is a historic town and industrial center located on the Oka River in Tula Oblast, Russia.
|
E476560
|
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: Aleksin | Statement: [Tula Oblast, hasMajorCity, Aleksin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aleksin Context triple: [Tula Oblast, hasMajorCity, Aleksin]
-
A.
Alek
Alek is a common diminutive or short form of the given name Aleksander, used in various Slavic and European languages.
-
B.
Vasilevsky
Vasilevsky is a Russian surname most prominently associated with Aleksandr Vasilevsky, a leading Soviet military commander and Marshal of the Soviet Union during World War II.
-
C.
Aleksandr Nazarov
Aleksandr Nazarov is a Russian politician best known for serving as the first governor of the Chukotka Autonomous Okrug in the post-Soviet era.
-
D.
Vitaly
Vitaly is a masculine given name of Slavic origin, commonly used in Russian-speaking countries.
-
E.
Nikita Anisimov
Nikita Anisimov is a Russian academic and university administrator who serves as the rector of the National Research University Higher School of Economics (HSE) in Moscow.
- 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: Aleksin Triple: [Tula Oblast, hasMajorCity, Aleksin]
Generated description
Aleksin is a historic town and industrial center located on the Oka River in Tula Oblast, Russia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Aleksin Target entity description: Aleksin is a historic town and industrial center located on the Oka River in Tula Oblast, Russia.
-
A.
Alek
Alek is a common diminutive or short form of the given name Aleksander, used in various Slavic and European languages.
-
B.
Vasilevsky
Vasilevsky is a Russian surname most prominently associated with Aleksandr Vasilevsky, a leading Soviet military commander and Marshal of the Soviet Union during World War II.
-
C.
Aleksandr Nazarov
Aleksandr Nazarov is a Russian politician best known for serving as the first governor of the Chukotka Autonomous Okrug in the post-Soviet era.
-
D.
Vitaly
Vitaly is a masculine given name of Slavic origin, commonly used in Russian-speaking countries.
-
E.
Nikita Anisimov
Nikita Anisimov is a Russian academic and university administrator who serves as the rector of the National Research University Higher School of Economics (HSE) in Moscow.
- 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_69bd440f71348190b99938e59fb7f9a1 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e07ca10819083f80f12374544b1 |
completed | March 20, 2026, 3:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be681a3d7881908120dc642af3f58a |
completed | March 21, 2026, 9:42 a.m. |
| NEDg | Description generation | batch_69be6892c02481908dc64c7e84aac3b2 |
completed | March 21, 2026, 9:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be695116788190903fbd5e375bd31d |
completed | March 21, 2026, 9:48 a.m. |
Created at: March 20, 2026, 1:28 p.m.