Triple

T32705679
Position Surface form Disambiguated ID Type / Status
Subject SCMAGLEV and Railway Park E836266 entity
Predicate hasExhibit P35 FINISHED
Object MLX01 maglev vehicle
The MLX01 maglev vehicle is a Japanese experimental high-speed magnetic levitation train prototype developed to test and advance superconducting maglev technology.
E1002376 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: MLX01 maglev vehicle | Statement: [SCMAGLEV and Railway Park, hasExhibit, MLX01 maglev vehicle]
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: MLX01 maglev vehicle
Triple: [SCMAGLEV and Railway Park, hasExhibit, MLX01 maglev vehicle]
Generated description
The MLX01 maglev vehicle is a Japanese experimental high-speed magnetic levitation train prototype developed to test and advance superconducting maglev technology.

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_69f3493446148190819541f3ffe79975 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6c850ea5881909f5e24e12a07c439 completed May 3, 2026, 4 a.m.
NED1 Entity disambiguation (via context triple) batch_6a349ec72c9081908e83fa2d82e3d815 completed June 19, 2026, 1:43 a.m.
NEDg Description generation batch_6a349f3636f08190a0bfda93e0d62a24 completed June 19, 2026, 1:45 a.m.
NED2 Entity disambiguation (via description) batch_6a34a0b3ef448190b6e68eda0410de80 completed June 19, 2026, 1:51 a.m.
Created at: May 1, 2026, 1:10 a.m.