Triple

T17437012
Position Surface form Disambiguated ID Type / Status
Subject The Quiet Duel E424024 entity
Predicate cinematographyBy P1953 FINISHED
Object Soichi Aisaka
Soichi Aisaka was a Japanese cinematographer known for his work on classic mid-20th-century films, including collaborations with prominent directors such as Akira Kurosawa.
E2288715 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: Soichi Aisaka | Statement: [The Quiet Duel, cinematographyBy, Soichi Aisaka]
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: Soichi Aisaka
Triple: [The Quiet Duel, cinematographyBy, Soichi Aisaka]
Generated description
Soichi Aisaka was a Japanese cinematographer known for his work on classic mid-20th-century films, including collaborations with prominent directors such as Akira Kurosawa.

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_69d889d88b6081908bada047f5b3ba51 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e44ff468a08190bdc5cebb8162b4ba completed April 19, 2026, 3:45 a.m.
NED1 Entity disambiguation (via context triple) batch_6a5ad71b60008190906b4ad76ae76a36 completed July 18, 2026, 1:30 a.m.
NEDg Description generation batch_6a5ad82f2cc48190bd8d8a49a2d410a0 completed July 18, 2026, 1:34 a.m.
NED2 Entity disambiguation (via description) batch_6a5ad886e008819086121eadc086217b completed July 18, 2026, 1:36 a.m.
Created at: April 10, 2026, 5:46 a.m.