Triple

T24927405
Position Surface form Disambiguated ID Type / Status
Subject Kęty E618896 entity
Predicate hasNotablePerson P304 FINISHED
Object John Cantius
John Cantius (Saint John Cantius or John of Kęty) was a 15th-century Polish priest, scholar, and professor at the Jagiellonian University, venerated in the Catholic Church for his piety, charity, and learning.
E1655632 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 Cantius | Statement: [Kęty, hasNotablePerson, John Cantius]
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 Cantius
Triple: [Kęty, hasNotablePerson, John Cantius]
Generated description
John Cantius (Saint John Cantius or John of Kęty) was a 15th-century Polish priest, scholar, and professor at the Jagiellonian University, venerated in the Catholic Church for his piety, charity, and learning.

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_69e2fab9edd88190b86004a78a28bc20 completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f423b0ac18819091801893849a2ca6 completed May 1, 2026, 3:53 a.m.
NED1 Entity disambiguation (via context triple) batch_6a103336beb481908d94222f97900caf completed May 22, 2026, 10:43 a.m.
NEDg Description generation batch_6a1033be69f88190988f54e89df5438a completed May 22, 2026, 10:45 a.m.
NED2 Entity disambiguation (via description) batch_6a10346cdcac8190865eb3c1b86c9c2c completed May 22, 2026, 10:48 a.m.
Created at: April 18, 2026, 5:29 a.m.