Triple

T27454027
Position Surface form Disambiguated ID Type / Status
Subject Mary of Baux-Orange E692533 entity
Predicate child P120 FINISHED
Object Margaret of Chalon
Margaret of Chalon was a medieval French noblewoman of the House of Chalon, notable as the daughter of Mary of Baux-Orange and a member of the high aristocracy of Provence and Burgundy.
 E2087052 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: Margaret of Chalon | Statement: [Mary of Baux-Orange, child, Margaret of Chalon]
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: Margaret of Chalon
Triple: [Mary of Baux-Orange, child, Margaret of Chalon]
Generated description
Margaret of Chalon was a medieval French noblewoman of the House of Chalon, notable as the daughter of Mary of Baux-Orange and a member of the high aristocracy of Provence and 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_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_6a36d5bc2318819092b8a32b3af63d72 completed June 20, 2026, 6:02 p.m.
NEDg Description generation batch_6a36d69820ac8190a102919d7bfbbeeb completed June 20, 2026, 6:06 p.m.
NED2 Entity disambiguation (via description) batch_6a36d733c79c819099c6c8306ac9a7bd completed June 20, 2026, 6:08 p.m.
Created at: April 27, 2026, 12:48 p.m.