Triple

T27316435
Position Surface form Disambiguated ID Type / Status
Subject Jean Casimir-Perier E689359 entity
Predicate mother P120 FINISHED
Object Mathilde de Villeneuve-Bargemon
Mathilde de Villeneuve-Bargemon was a French aristocrat best known as the mother of Jean Casimir-Perier, who served as President of France in the late 19th century.
 E1776990 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: Mathilde de Villeneuve-Bargemon | Statement: [Jean Casimir-Perier, mother, Mathilde de Villeneuve-Bargemon]
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: Mathilde de Villeneuve-Bargemon
Triple: [Jean Casimir-Perier, mother, Mathilde de Villeneuve-Bargemon]
Generated description
Mathilde de Villeneuve-Bargemon was a French aristocrat best known as the mother of Jean Casimir-Perier, who served as President of France in the late 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_69ef355c53a08190a8a92e355a7ce115 completed April 27, 2026, 10:07 a.m.
NER Named-entity recognition batch_69f627b69d0881908e5da0d2d6acedae completed May 2, 2026, 4:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12c5919e90819080cdcb589df5c3da completed May 24, 2026, 9:32 a.m.
NEDg Description generation batch_6a12c6815f2081908101a3e47b812298 completed May 24, 2026, 9:36 a.m.
NED2 Entity disambiguation (via description) batch_6a12c6fb1e588190997a8fd210b5e52b completed May 24, 2026, 9:38 a.m.
Created at: April 27, 2026, 11:30 a.m.