Triple

T19987469
Position Surface form Disambiguated ID Type / Status
Subject Paper Mario: The Thousand-Year Door E493970 entity
Predicate director P255 FINISHED
Object Ryota Kawade
Ryota Kawade is a Japanese video game director best known for leading development on Nintendo’s acclaimed role-playing game Paper Mario: The Thousand-Year Door.
E2293272 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: Ryota Kawade | Statement: [Paper Mario: The Thousand-Year Door, director, Ryota Kawade]
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: Ryota Kawade
Triple: [Paper Mario: The Thousand-Year Door, director, Ryota Kawade]
Generated description
Ryota Kawade is a Japanese video game director best known for leading development on Nintendo’s acclaimed role-playing game Paper Mario: The Thousand-Year Door.

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_69da626a67648190af9653832a3aeced completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e65fdd1a5c8190af756632aac38bf4 completed April 20, 2026, 5:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a7a864468188190a110e1a1cf42462b completed Aug. 11, 2026, 2:17 a.m.
NEDg Description generation batch_6a7a86f560ac8190a7f669fb7ceb084f completed Aug. 11, 2026, 2:20 a.m.
NED2 Entity disambiguation (via description) batch_6a7a874bc39881909272de1372b62ce0 completed Aug. 11, 2026, 2:22 a.m.
Created at: April 11, 2026, 3:29 p.m.