Triple

T31629364
Position Surface form Disambiguated ID Type / Status
Subject Miyuki Shirogane E807118 entity
Predicate voiceActorJapanese P99957 FINISHED
Object Makoto Furukawa
Makoto Furukawa is a Japanese voice actor known for prominent roles in anime such as Saitama in "One-Punch Man" and Miyuki Shirogane in "Kaguya-sama: Love Is War."
E2295456 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: Makoto Furukawa | Statement: [Miyuki Shirogane, voiceActorJapanese, Makoto Furukawa]
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: Makoto Furukawa
Triple: [Miyuki Shirogane, voiceActorJapanese, Makoto Furukawa]
Generated description
Makoto Furukawa is a Japanese voice actor known for prominent roles in anime such as Saitama in "One-Punch Man" and Miyuki Shirogane in "Kaguya-sama: Love Is War."

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_69f348d892948190915f8facacb9568c completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a8e303148190b0f8959045db3073 completed May 3, 2026, 1:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a7d585e01508190b86b907b1e20e604 completed Aug. 13, 2026, 5:38 a.m.
NEDg Description generation batch_6a7d58ccabe4819082ea8ef03f87b593 completed Aug. 13, 2026, 5:40 a.m.
NED2 Entity disambiguation (via description) batch_6a7d593d46888190b430ef96739789e2 completed Aug. 13, 2026, 5:42 a.m.
Created at: April 30, 2026, 10:44 p.m.