Triple

T17437062
Position Surface form Disambiguated ID Type / Status
Subject Dodes'ka-den E424025 entity
Predicate castMember P1668 FINISHED
Object Kei Sato
Kei Sato was a Japanese actor known for his work in mid-20th-century cinema, particularly in art-house and socially conscious films.
E2288950 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: Kei Sato | Statement: [Dodes'ka-den, castMember, Kei Sato]
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: Kei Sato
Triple: [Dodes'ka-den, castMember, Kei Sato]
Generated description
Kei Sato was a Japanese actor known for his work in mid-20th-century cinema, particularly in art-house and socially conscious films.

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_6a5af1ed1c848190b74250eb9cb49f75 completed July 18, 2026, 3:24 a.m.
NEDg Description generation batch_6a5af4333c54819090172b714eb78690 completed July 18, 2026, 3:34 a.m.
NED2 Entity disambiguation (via description) batch_6a5af63dea1081908a07b4e3f4629508 completed July 18, 2026, 3:42 a.m.
Created at: April 10, 2026, 5:46 a.m.