Triple

T36045309
Position Surface form Disambiguated ID Type / Status
Subject Citroën Xsara WRC E1042654 entity
Predicate notableCoDriver P7375 FINISHED
Object Daniel Elena
Daniel Elena is a highly successful Monegasque rally co-driver best known for his long-time partnership with Sébastien Loeb, with whom he won multiple World Rally Championship titles.
E2167509 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: Daniel Elena | Statement: [Citroën Xsara WRC, notableCoDriver, Daniel Elena]
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: Daniel Elena
Triple: [Citroën Xsara WRC, notableCoDriver, Daniel Elena]
Generated description
Daniel Elena is a highly successful Monegasque rally co-driver best known for his long-time partnership with Sébastien Loeb, with whom he won multiple World Rally Championship titles.

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_69f76e2e41f8819091f9fb0536920fec completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7b1c494cc8190800d92dc4ad8ec79 completed May 3, 2026, 8:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38cb9b762c8190a6f4734993e0d171 completed June 22, 2026, 5:43 a.m.
NEDg Description generation batch_6a38cc6481308190aeecc6bd2568377b completed June 22, 2026, 5:47 a.m.
NED2 Entity disambiguation (via description) batch_6a38ccf8e8b48190ac2f931ffa6ff800 completed June 22, 2026, 5:49 a.m.
Created at: May 3, 2026, 4:07 p.m.