Triple

T27454019
Position Surface form Disambiguated ID Type / Status
Subject Mary of Baux-Orange E692533 entity
Predicate mother P120 FINISHED
Object Joanna of Geneva
Joanna of Geneva was a medieval noblewoman from the House of Geneva, known primarily as the mother of Mary of Baux-Orange and for her ties to the aristocracy of southeastern France.
 E1774894 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: Joanna of Geneva | Statement: [Mary of Baux-Orange, mother, Joanna of Geneva]
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: Joanna of Geneva
Triple: [Mary of Baux-Orange, mother, Joanna of Geneva]
Generated description
Joanna of Geneva was a medieval noblewoman from the House of Geneva, known primarily as the mother of Mary of Baux-Orange and for her ties to the aristocracy of southeastern 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_69ef5207903881909427745cda05d27a completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62dc7c830819092e3f52733c8f60e completed May 2, 2026, 5 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12bbd840308190acc801fdee9c03bd completed May 24, 2026, 8:50 a.m.
NEDg Description generation batch_6a12bc771b0481909cca1c87c805f0de completed May 24, 2026, 8:53 a.m.
NED2 Entity disambiguation (via description) batch_6a12bd38f1948190a0b1f05ff28d8289 completed May 24, 2026, 8:56 a.m.
Created at: April 27, 2026, 12:48 p.m.