Triple

T8941764
Position Surface form Disambiguated ID Type / Status
Subject Alexander Dovzhenko E212917 entity
Predicate notableWork P4 FINISHED
Object Aerograd
Aerograd is a 1935 Soviet science fiction and propaganda film directed by Alexander Dovzhenko, set in a futuristic Far Eastern border town threatened by foreign and internal enemies.
 E767208 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: Aerograd | Statement: [Alexander Dovzhenko, notableWork, Aerograd]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Aerograd
Context triple: [Alexander Dovzhenko, notableWork, Aerograd]
  • A. Equair
    Equair is an Ecuadorian airline that operated domestic passenger flights, notably serving routes from Guayaquil and Quito.
  • B. Avion
    Avion is a commune in the Pas-de-Calais department in northern France.
  • C. IrAero
    IrAero is a Russian regional airline that operates passenger and cargo flights across Siberia, the Russian Far East, and neighboring countries.
  • D. Arajet
    Arajet is a Dominican low-cost airline based in Santo Domingo that operates flights across the Caribbean and the Americas.
  • E. Altior
    Altior is a celebrated British National Hunt racehorse renowned for his exceptional unbeaten streak over fences and multiple Grade 1 victories.
  • 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: Aerograd
Triple: [Alexander Dovzhenko, notableWork, Aerograd]
Generated description
Aerograd is a 1935 Soviet science fiction and propaganda film directed by Alexander Dovzhenko, set in a futuristic Far Eastern border town threatened by foreign and internal enemies.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Aerograd
Target entity description: Aerograd is a 1935 Soviet science fiction and propaganda film directed by Alexander Dovzhenko, set in a futuristic Far Eastern border town threatened by foreign and internal enemies.
  • A. Equair
    Equair is an Ecuadorian airline that operated domestic passenger flights, notably serving routes from Guayaquil and Quito.
  • B. Avion
    Avion is a commune in the Pas-de-Calais department in northern France.
  • C. IrAero
    IrAero is a Russian regional airline that operates passenger and cargo flights across Siberia, the Russian Far East, and neighboring countries.
  • D. Arajet
    Arajet is a Dominican low-cost airline based in Santo Domingo that operates flights across the Caribbean and the Americas.
  • E. Altior
    Altior is a celebrated British National Hunt racehorse renowned for his exceptional unbeaten streak over fences and multiple Grade 1 victories.
  • 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_69ca839694c88190b324ffeb43d23b08 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc66b9c14c8190b80c3df0cdba2747 completed April 1, 2026, 12:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc1f418708190b1272209f61e3a51 completed April 3, 2026, 1:34 p.m.
NEDg Description generation batch_69cfc25fdf3481909d9821f7728b0c5b completed April 3, 2026, 1:36 p.m.
NED2 Entity disambiguation (via description) batch_69cfc2e808408190b9bc44ed21fc67d9 completed April 3, 2026, 1:38 p.m.
Created at: March 30, 2026, 6:58 p.m.