Triple

T20217832
Position Surface form Disambiguated ID Type / Status
Subject The Legend of Zelda: Tears of the Kingdom E495173 entity
Predicate composer P1361 FINISHED
Object Maasa Miyoshi
Maasa Miyoshi is a Japanese video game composer best known for contributing to the soundtrack of Nintendo’s The Legend of Zelda: Tears of the Kingdom.
E1593564 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: Maasa Miyoshi | Statement: [The Legend of Zelda: Tears of the Kingdom, composer, Maasa Miyoshi]
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: Maasa Miyoshi
Triple: [The Legend of Zelda: Tears of the Kingdom, composer, Maasa Miyoshi]
Generated description
Maasa Miyoshi is a Japanese video game composer best known for contributing to the soundtrack of Nintendo’s The Legend of Zelda: Tears of the Kingdom.

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_69da626cff80819097b530718a7c98b6 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e66edaeeb08190bb74bd4a10aceca2 completed April 20, 2026, 6:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0f4531481c81908b3c1e81d1322994 completed May 21, 2026, 5:47 p.m.
NEDg Description generation batch_6a0f46b69d288190b3fb6dcea9fb44b5 completed May 21, 2026, 5:53 p.m.
NED2 Entity disambiguation (via description) batch_6a0f479575a48190a63dd376b8fec617 completed May 21, 2026, 5:57 p.m.
Created at: April 11, 2026, 11:39 p.m.