Triple

T38345118
Position Surface form Disambiguated ID Type / Status
Subject Good Luck!! E1041517 entity
Predicate director P255 FINISHED
Object Toshiaki Shimizu
Toshiaki Shimizu is a Japanese film and television director best known for his work on the drama series "Good Luck!!".
E2297757 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: Toshiaki Shimizu | Statement: [Good Luck!!, director, Toshiaki Shimizu]
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: Toshiaki Shimizu
Triple: [Good Luck!!, director, Toshiaki Shimizu]
Generated description
Toshiaki Shimizu is a Japanese film and television director best known for his work on the drama series "Good Luck!!".

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_69f76e2ad95481908c920c0e5c1c3e26 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69fcc6ef726c8190be72dcc8b557873c completed May 7, 2026, 5:07 p.m.
NED1 Entity disambiguation (via context triple) batch_6a83cf57dce08190952e61e304fd52a7 completed Aug. 18, 2026, 3:19 a.m.
NEDg Description generation batch_6a83cfbee488819081e426f5a12710e8 completed Aug. 18, 2026, 3:21 a.m.
NED2 Entity disambiguation (via description) batch_6a83cfebdc00819093ac1bc5765afd01 completed Aug. 18, 2026, 3:22 a.m.
Created at: May 3, 2026, 4:30 p.m.