Triple

T25251402
Position Surface form Disambiguated ID Type / Status
Subject Anthony, bastard of Burgundy E632749 entity
Predicate spouse P13 FINISHED
Object Marie de la Viesville
Marie de la Viesville was a noblewoman of the Burgundian court known primarily as the wife of Anthony, the illegitimate son of Philip the Good, Duke of Burgundy.
E1743240 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 la Viesville | Statement: [Anthony, bastard of Burgundy, spouse, Marie de la Viesville]
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 la Viesville
Triple: [Anthony, bastard of Burgundy, spouse, Marie de la Viesville]
Generated description
Marie de la Viesville was a noblewoman of the Burgundian court known primarily as the wife of Anthony, the illegitimate son of Philip the Good, Duke of Burgundy.

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_69e75a8fdd3881909ba0b05aa5da92a7 completed April 21, 2026, 11:08 a.m.
NER Named-entity recognition batch_69f4808b8ab08190b48cca8408c88bdf completed May 1, 2026, 10:29 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1212fce3608190a3528195d02f18fa completed May 23, 2026, 8:50 p.m.
NEDg Description generation batch_6a12150278448190b2538abe4f8d2e6f completed May 23, 2026, 8:58 p.m.
NED2 Entity disambiguation (via description) batch_6a1215e830648190afc5de590a2a2cc1 completed May 23, 2026, 9:02 p.m.
Created at: April 21, 2026, 1:11 p.m.