Triple

T33362110
Position Surface form Disambiguated ID Type / Status
Subject Extravagantes Communes E854248 entity
Predicate editor P1954 FINISHED
Object Jean Chappuis
Jean Chappuis was a late medieval editor and humanist scholar known for preparing influential printed editions of canon law collections, including the Extravagantes Communes.
E2050974 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: Jean Chappuis | Statement: [Extravagantes Communes, editor, Jean Chappuis]
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: Jean Chappuis
Triple: [Extravagantes Communes, editor, Jean Chappuis]
Generated description
Jean Chappuis was a late medieval editor and humanist scholar known for preparing influential printed editions of canon law collections, including the Extravagantes Communes.

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_69f3496bda8c8190bfc8fade9d1b791c completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6dfcafd0c81908d86662948c539d6 completed May 3, 2026, 5:40 a.m.
NED1 Entity disambiguation (via context triple) batch_6a35814410208190bb32ff8748e814d8 completed June 19, 2026, 5:49 p.m.
NEDg Description generation batch_6a35825cda3c8190a734db82e560c4a0 completed June 19, 2026, 5:54 p.m.
NED2 Entity disambiguation (via description) batch_6a358358e7f88190a63c13768f9b5e18 completed June 19, 2026, 5:58 p.m.
Created at: May 1, 2026, 1:34 a.m.