Triple

T21902035
Position Surface form Disambiguated ID Type / Status
Subject Moscow Does Not Believe in Tears E540832 entity
Predicate cinematographyBy P1953 FINISHED
Object Igor Slabnevich
Igor Slabnevich was a Soviet cinematographer best known for his work on the Academy Award–winning film "Moscow Does Not Believe in Tears."
E1765659 NE FINISHED

How this triple was built (2 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: Igor Slabnevich | Statement: [Moscow Does Not Believe in Tears, cinematographyBy, Igor Slabnevich]
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: Igor Slabnevich
Triple: [Moscow Does Not Believe in Tears, cinematographyBy, Igor Slabnevich]
Generated description
Igor Slabnevich was a Soviet cinematographer best known for his work on the Academy Award–winning film "Moscow Does Not Believe in Tears."

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_69e0c47b4e8c81908c8076eaa4c8e4f2 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f121d2d63c819090e115708aa4dbf8 completed April 28, 2026, 9:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a129c728d7881909581db8cbe44e1e5 completed May 24, 2026, 6:36 a.m.
NEDg Description generation batch_6a129d1d3ac481908bfc02f55a5692eb completed May 24, 2026, 6:39 a.m.
NED2 Entity disambiguation (via description) batch_6a129ea4d28481909e42a9859efa9309 completed May 24, 2026, 6:45 a.m.
Created at: April 16, 2026, 7:21 p.m.