Triple

T15795094
Position Surface form Disambiguated ID Type / Status
Subject Gogotur and Apshina E382956 entity
Predicate hasCharacter P2308 FINISHED
Object Apshina
Apshina is a fictional character from the work "Gogotur and Apshina," likely serving as one of its central figures.
 E1177679 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: Apshina | Statement: [Gogotur and Apshina, hasCharacter, Apshina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Apshina
Context triple: [Gogotur and Apshina, hasCharacter, Apshina]
  • A. Kasimov
    Kasimov is a historic town in central Russia known for its Tatar heritage, medieval architecture, and location on the Oka River.
  • B. Makeyevka
    Makeyevka is an industrial city in eastern Ukraine’s Donetsk Oblast, historically known for its coal mining and metallurgical industries.
  • C. Yamburg
    Yamburg is the former name of the Russian town now known as Kingisepp, located in Leningrad Oblast near the border with Estonia.
  • D. Astapovo
    Astapovo is a small Russian railway station village historically known as the place where the writer Leo Tolstoy died in 1910.
  • E. Safonovo
    Safonovo is a small industrial town in western Russia known for its role in the regional energy and manufacturing sectors within Smolensk Oblast.
  • 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: Apshina
Triple: [Gogotur and Apshina, hasCharacter, Apshina]
Generated description
Apshina is a fictional character from the work "Gogotur and Apshina," likely serving as one of its central figures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Apshina
Target entity description: Apshina is a fictional character from the work "Gogotur and Apshina," likely serving as one of its central figures.
  • A. Kasimov
    Kasimov is a historic town in central Russia known for its Tatar heritage, medieval architecture, and location on the Oka River.
  • B. Makeyevka
    Makeyevka is an industrial city in eastern Ukraine’s Donetsk Oblast, historically known for its coal mining and metallurgical industries.
  • C. Yamburg
    Yamburg is the former name of the Russian town now known as Kingisepp, located in Leningrad Oblast near the border with Estonia.
  • D. Astapovo
    Astapovo is a small Russian railway station village historically known as the place where the writer Leo Tolstoy died in 1910.
  • E. Safonovo
    Safonovo is a small industrial town in western Russia known for its role in the regional energy and manufacturing sectors within Smolensk Oblast.
  • 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_69d86da16e188190b89af699f1ed0bfe completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e0b4dc887081909d682ae153f06d97 completed April 16, 2026, 10:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff90ab23048190a6d976c9a3143647 completed May 9, 2026, 7:53 p.m.
NEDg Description generation batch_69ff93c259e481908d419c101512c140 completed May 9, 2026, 8:06 p.m.
NED2 Entity disambiguation (via description) batch_69ff9458a1388190bfb2b1ecbbf5ebdd completed May 9, 2026, 8:08 p.m.
Created at: April 10, 2026, 4:48 a.m.