Triple

T3155865
Position Surface form Disambiguated ID Type / Status
Subject Stade Français E65983 entity
Predicate hasRivalryWith P893 FINISHED
Object Racing 92 E194433 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: Racing 92 | Statement: [Stade Français, hasRivalryWith, Racing 92]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Racing 92
Context triple: [Stade Français, hasRivalryWith, Racing 92]
  • A. Racing 92 rugby union chosen
    Racing 92 rugby union is a professional French rugby union club based in the Paris region that competes in the Top 14 and European competitions.
  • B. Stade Français
    Stade Français is a historic Parisian multi-sport club best known internationally for its top-tier professional rugby union team competing in France’s premier league.
  • C. Stade Toulousain
    Stade Toulousain is a French professional rugby union club based in Toulouse, renowned as one of Europe’s most successful and decorated rugby teams.
  • D. USAP Perpignan
    USAP Perpignan is a professional rugby union club based in Perpignan, France, known for its strong tradition in French and European competitions.
  • E. ASM Clermont Auvergne
    ASM Clermont Auvergne is a professional French rugby union club competing in the Top 14 and based in the city of Clermont-Ferrand.
  • 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_69ad8584485081909ed529e890cadc4a completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada5e97548819084643586fff2e3cb completed March 8, 2026, 4:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2e81ab30481909d73aed49d4192a3 completed March 12, 2026, 4:21 p.m.
Created at: March 8, 2026, 3:05 p.m.