Triple

T26383143
Position Surface form Disambiguated ID Type / Status
Subject Marie of Berry E663196 entity
Predicate positionHeld P8 FINISHED
Object Duchess consort of Berry
The Duchess consort of Berry was a noble title in the French royal hierarchy held by the wife of the Duke of Berry, typically associated with significant status and influence at the French court.
E1728510 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: Duchess consort of Berry | Statement: [Marie of Berry, positionHeld, Duchess consort of Berry]
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: Duchess consort of Berry
Triple: [Marie of Berry, positionHeld, Duchess consort of Berry]
Generated description
The Duchess consort of Berry was a noble title in the French royal hierarchy held by the wife of the Duke of Berry, typically associated with significant status and influence at the French court.

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_69ee88374adc81909868f3bab374a32f completed April 26, 2026, 9:48 p.m.
NER Named-entity recognition batch_69f610779e3481909bda4d2b1c5c4cb0 completed May 2, 2026, 2:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11bb0e7ebc8190971e40198a3aa686 completed May 23, 2026, 2:34 p.m.
NEDg Description generation batch_6a11be5f621881908d83370dd283a10f completed May 23, 2026, 2:49 p.m.
NED2 Entity disambiguation (via description) batch_6a11bf7e1de48190ba8ed044628d5bf7 completed May 23, 2026, 2:53 p.m.
Created at: April 26, 2026, 11:20 p.m.