Triple

T32621291
Position Surface form Disambiguated ID Type / Status
Subject Kamen Rider: Dragon Knight E833930 entity
Predicate hasMainCharacter P1183 FINISHED
Object Kit Taylor
Kit Taylor is the protagonist of the American tokusatsu series Kamen Rider: Dragon Knight, who becomes Kamen Rider Dragon Knight to battle invading monsters from another dimension.
E2020607 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: Kit Taylor | Statement: [Kamen Rider: Dragon Knight, hasMainCharacter, Kit Taylor]
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: Kit Taylor
Triple: [Kamen Rider: Dragon Knight, hasMainCharacter, Kit Taylor]
Generated description
Kit Taylor is the protagonist of the American tokusatsu series Kamen Rider: Dragon Knight, who becomes Kamen Rider Dragon Knight to battle invading monsters from another dimension.

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_69f3492ccc80819086ef7d26e9786647 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6c6efdadc81908e02907d9b22400c completed May 3, 2026, 3:54 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34a79742b08190a0ddd31d3212e1a3 completed June 19, 2026, 2:21 a.m.
NEDg Description generation batch_6a34a84e9e3881909614d79de44dd3cc completed June 19, 2026, 2:24 a.m.
NED2 Entity disambiguation (via description) batch_6a34a8dde9f48190b9912c18f2470edf completed June 19, 2026, 2:26 a.m.
Created at: May 1, 2026, 1:06 a.m.