Triple

T26408858
Position Surface form Disambiguated ID Type / Status
Subject Gilles Van Assche E663903 entity
Predicate coWorkedWith P26607 FINISHED
Object Michaël Peeters
Michaël Peeters is a cryptographer known for his work on symmetric-key algorithms and contributions to the design of the Keccak hash function, which became the SHA-3 standard.
E663902 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: Michaël Peeters | Statement: [Gilles Van Assche, coWorkedWith, Michaël Peeters]
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: Michaël Peeters
Triple: [Gilles Van Assche, coWorkedWith, Michaël Peeters]
Generated description
Michaël Peeters is a cryptographer known for his work on symmetric-key algorithms and contributions to the design of the Keccak hash function, which became the SHA-3 standard.

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_69ee883931888190901be96d75ee23cc completed April 26, 2026, 9:48 p.m.
NER Named-entity recognition batch_69f610faa77081908956b6e8b5b1570c completed May 2, 2026, 2:58 p.m.
NED1 Entity disambiguation (via context triple) batch_6a123a8f02b88190b89c824f50bb3c68 completed May 23, 2026, 11:38 p.m.
NEDg Description generation batch_6a123b542138819086f001a5c2dcd76b completed May 23, 2026, 11:42 p.m.
NED2 Entity disambiguation (via description) batch_6a123bf84c28819096727646233344f5 completed May 23, 2026, 11:44 p.m.
Created at: April 26, 2026, 11:36 p.m.