Triple

T9775106
Position Surface form Disambiguated ID Type / Status
Subject Dan Carter E237225 entity
Predicate clubTeam P19122 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: [Dan Carter, clubTeam, Racing 92]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Racing 92
Context triple: [Dan Carter, clubTeam, 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. Montpellier Hérault Rugby
    Montpellier Hérault Rugby is a professional French rugby union club based in Montpellier that competes in the country’s top division, the Top 14.
  • E. Biarritz Olympique
    Biarritz Olympique is a French professional rugby union club based in the coastal city of Biarritz, known for its strong history in domestic and European competitions.
  • 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_69ca84d975a08190aab25b02a89bdab3 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cda13148288190bcbb3b4a066d9fc1 completed April 1, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69d22866aea48190933524839241869a completed April 5, 2026, 9:16 a.m.
Created at: March 30, 2026, 8:26 p.m.