Triple

T36426101
Position Surface form Disambiguated ID Type / Status
Subject Society of American Magicians E897306 entity
Predicate hasNotableMember P304 FINISHED
Object Juan Tamariz
Juan Tamariz is a renowned Spanish magician and theorist celebrated worldwide for his innovative card magic, influential writings, and charismatic performances.
E2284824 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: Juan Tamariz | Statement: [Society of American Magicians, hasNotableMember, Juan Tamariz]
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: Juan Tamariz
Triple: [Society of American Magicians, hasNotableMember, Juan Tamariz]
Generated description
Juan Tamariz is a renowned Spanish magician and theorist celebrated worldwide for his innovative card magic, influential writings, and charismatic performances.

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_69f76e559b10819099d6655a6e14587c completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7bd4da0ec819088cafa6fb8661cc2 completed May 3, 2026, 9:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a44a358447c81908baf52ba9c329f15 completed July 1, 2026, 5:19 a.m.
NEDg Description generation batch_6a44a4259a588190ac5e415d6796c8f0 completed July 1, 2026, 5:22 a.m.
NED2 Entity disambiguation (via description) batch_6a44a59a4e7081909521e8a5af0f7e13 completed July 1, 2026, 5:28 a.m.
Created at: May 3, 2026, 4:10 p.m.