Triple

T16789040
Position Surface form Disambiguated ID Type / Status
Subject Robert de Sorbon E408055 entity
Predicate familyName P18 FINISHED
Object de Sorbon E408055 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: de Sorbon | Statement: [Robert de Sorbon, familyName, de Sorbon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: de Sorbon
Context triple: [Robert de Sorbon, familyName, de Sorbon]
  • A. de Sorbon chosen
    de Sorbon is the surname of Robert de Sorbon, the 13th-century French theologian and founder of the Sorbonne college in Paris.
  • B. Sorbon
    Sorbon is a small commune in the Ardennes department of northern France, historically notable as the birthplace of theologian Robert de Sorbon, founder of the Sorbonne.
  • C. La Sorbonne
    La Sorbonne is a historic university building in Paris that has long served as a central symbol of French higher education and intellectual life.
  • D. Sorbonne University
    Sorbonne University is a major public research university in Paris renowned for its historic humanities, science, and medical faculties.
  • E. College of Louis le Grand
    The College of Louis le Grand is a prestigious Parisian secondary school and former Jesuit institution historically known for educating many prominent political, intellectual, and literary figures.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d8839270588190886720d9519bbf8f completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3b2a50e18819090a30e1f38e520e0 completed April 18, 2026, 4:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00ab09932c8190ba16a349c4938c16 completed May 10, 2026, 3:58 p.m.
Created at: April 10, 2026, 5:22 a.m.