Triple

T16544424
Position Surface form Disambiguated ID Type / Status
Subject France at the European Figure Skating Championships E401904 entity
Predicate notableSkater P10392 FINISHED
Object Gwendal Peizerat
Gwendal Peizerat is a French ice dancer best known for winning the 2002 Olympic gold medal and multiple World and European titles with partner Marina Anissina.
E1892743 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: Gwendal Peizerat | Statement: [France at the European Figure Skating Championships, notableSkater, Gwendal Peizerat]
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: Gwendal Peizerat
Triple: [France at the European Figure Skating Championships, notableSkater, Gwendal Peizerat]
Generated description
Gwendal Peizerat is a French ice dancer best known for winning the 2002 Olympic gold medal and multiple World and European titles with partner Marina Anissina.

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_69d88384bc30819084229e7dcdc39a41 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e34560daf08190b353b415d8ab280d completed April 18, 2026, 8:48 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2713e7d1088190a1bed559fb1658fb completed June 8, 2026, 7:11 p.m.
NEDg Description generation batch_6a2714fad8188190bf86af12ee777b53 completed June 8, 2026, 7:16 p.m.
NED2 Entity disambiguation (via description) batch_6a27198a097c8190aea66eba80acc1d8 completed June 8, 2026, 7:35 p.m.
Created at: April 10, 2026, 5:15 a.m.