Triple

T30699229
Position Surface form Disambiguated ID Type / Status
Subject University College of North Wales E781564 entity
Predicate hasAlumni P51 FINISHED
Object John Wynne Jones
John Wynne Jones was a Welsh scholar and clergyman associated with the University College of North Wales in the 19th century.
 E1930555 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 Wynne Jones | Statement: [University College of North Wales, hasAlumni, John Wynne Jones]
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 Wynne Jones
Triple: [University College of North Wales, hasAlumni, John Wynne Jones]
Generated description
John Wynne Jones was a Welsh scholar and clergyman associated with the University College of North Wales in the 19th century.

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_69f224ab24e08190991d6edb6df58e8b completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f68bdda9dc8190b949ac31d7a79273 completed May 2, 2026, 11:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a28b082c67481908669d61a9579ae08 completed June 10, 2026, 12:32 a.m.
NEDg Description generation batch_6a28b1ecd9448190a88b0f465f97a8ba completed June 10, 2026, 12:38 a.m.
NED2 Entity disambiguation (via description) batch_6a28b28e61f881908ea11ba5951789bd completed June 10, 2026, 12:40 a.m.
Created at: April 29, 2026, 8:34 p.m.