Triple

T27498079
Position Surface form Disambiguated ID Type / Status
Subject Anne de Joyeuse E694070 entity
Predicate mother P120 FINISHED
Object Marie de Batarnay
Marie de Batarnay was a French noblewoman of the late 16th century, best known as the mother of Anne de Joyeuse, a powerful favorite of King Henry III of France.
 E1799127 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: Marie de Batarnay | Statement: [Anne de Joyeuse, mother, Marie de Batarnay]
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: Marie de Batarnay
Triple: [Anne de Joyeuse, mother, Marie de Batarnay]
Generated description
Marie de Batarnay was a French noblewoman of the late 16th century, best known as the mother of Anne de Joyeuse, a powerful favorite of King Henry III of France.

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_69ef538370888190b1ddf53bb4831188 completed April 27, 2026, 12:16 p.m.
NER Named-entity recognition batch_69f62ec0c9888190a7f0de2aa4d1d0b1 completed May 2, 2026, 5:05 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15b86f2a28819086ea4d6973d95754 completed May 26, 2026, 3:12 p.m.
NEDg Description generation batch_6a15b930d3a48190b47c9a7921d3f9b5 completed May 26, 2026, 3:16 p.m.
NED2 Entity disambiguation (via description) batch_6a15bb82f47c8190bf0ec0ca187e3c4b completed May 26, 2026, 3:25 p.m.
Created at: April 27, 2026, 1:09 p.m.