Triple

T31781259
Position Surface form Disambiguated ID Type / Status
Subject Tyner E811207 entity
Predicate hasNotableBearer P458 FINISHED
Object John Tyner
John Tyner is an American traveler who gained national attention in 2010 for refusing an enhanced pat-down by TSA agents and popularizing the phrase “don’t touch my junk.”
 E1984460 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: John Tyner | Statement: [Tyner, hasNotableBearer, John Tyner]
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: John Tyner
Triple: [Tyner, hasNotableBearer, John Tyner]
Generated description
John Tyner is an American traveler who gained national attention in 2010 for refusing an enhanced pat-down by TSA agents and popularizing the phrase “don’t touch my junk.”

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_69f348e544a48190ab6e700b05f6438c completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6abe587e48190b95e4cc7d6968b5d completed May 3, 2026, 1:59 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2e8a1ec580819096e264dfc9d540e3 completed June 14, 2026, 11:01 a.m.
NEDg Description generation batch_6a2e8aba8d9481908df3439168b0f62f completed June 14, 2026, 11:04 a.m.
NED2 Entity disambiguation (via description) batch_6a2e8b6f6f2c819098e1787c61964edd completed June 14, 2026, 11:07 a.m.
Created at: April 30, 2026, 11:36 p.m.