Triple

T32823982
Position Surface form Disambiguated ID Type / Status
Subject Shusaku Arakawa E839504 entity
Predicate notableWork P4 FINISHED
Object The Mechanism of Meaning
The Mechanism of Meaning is a conceptual art project and book by Shusaku Arakawa (with Madeline Gins) that uses diagrams, text, and visual puzzles to challenge conventional perception and the construction of meaning.
 E2024251 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: The Mechanism of Meaning | Statement: [Shusaku Arakawa, notableWork, The Mechanism of Meaning]
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: The Mechanism of Meaning
Triple: [Shusaku Arakawa, notableWork, The Mechanism of Meaning]
Generated description
The Mechanism of Meaning is a conceptual art project and book by Shusaku Arakawa (with Madeline Gins) that uses diagrams, text, and visual puzzles to challenge conventional perception and the construction of meaning.

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_69f3493df9008190a8f5d843dcd77704 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6cdf462088190bd48ddbbcc4ddd5f completed May 3, 2026, 4:24 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34b1854b9c8190963e9f13eabeec68 completed June 19, 2026, 3:03 a.m.
NEDg Description generation batch_6a34b28809fc819090b804d470dba1b3 completed June 19, 2026, 3:07 a.m.
NED2 Entity disambiguation (via description) batch_6a34b3356258819086890e71c40d1f9b completed June 19, 2026, 3:10 a.m.
Created at: May 1, 2026, 1:15 a.m.