Triple

T35517513
Position Surface form Disambiguated ID Type / Status
Subject Association Sportive de Cannes Football E1026457 entity
Predicate hasNotableFormerPlayer P15460 FINISHED
Object Franck Durix
Franck Durix is a retired French professional footballer, best known as a creative midfielder who played in Ligue 1 and abroad during the late 1980s and 1990s.
E2143956 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: Franck Durix | Statement: [Association Sportive de Cannes Football, hasNotableFormerPlayer, Franck Durix]
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: Franck Durix
Triple: [Association Sportive de Cannes Football, hasNotableFormerPlayer, Franck Durix]
Generated description
Franck Durix is a retired French professional footballer, best known as a creative midfielder who played in Ligue 1 and abroad during the late 1980s and 1990s.

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_69f76dfe78b081908e2b14cb88dd8c00 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7979d5ee88190afbeb29e74f2d126 completed May 3, 2026, 6:44 p.m.
NED1 Entity disambiguation (via context triple) batch_6a384a3cc39c81908dd03d8f8b06b352 completed June 21, 2026, 8:31 p.m.
NEDg Description generation batch_6a384abe08a481909ea55117e7f7d120 completed June 21, 2026, 8:34 p.m.
NED2 Entity disambiguation (via description) batch_6a384b6644208190b1c18024a063846b completed June 21, 2026, 8:36 p.m.
Created at: May 3, 2026, 4:04 p.m.