Triple

T4612971
Position Surface form Disambiguated ID Type / Status
Subject Serena Williams E100801 entity
Predicate wonTournament P8060 FINISHED
Object French Open E158003 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: French Open | Statement: [Serena Williams, wonTournament, French Open]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: French Open
Context triple: [Serena Williams, wonTournament, French Open]
  • A. French Open chosen
    The French Open is one of tennis's four major Grand Slam tournaments, renowned for its clay courts and held annually at Roland Garros in Paris.
  • B. Stade Roland Garros
    Stade Roland Garros is a famous Parisian tennis complex best known as the venue for the French Open, one of the four Grand Slam tournaments.
  • C. Australian Open
    The Australian Open is one of tennis's four Grand Slam tournaments, held annually in Melbourne and known for its hard courts and intense summer conditions.
  • D. Wimbledon
    Wimbledon is a district in southwest London best known for hosting the prestigious annual Wimbledon tennis championships, the oldest tennis tournament in the world.
  • E. US Open (tennis)
    The US Open (tennis) is one of the four Grand Slam tournaments, a major annual hard-court championship that attracts the world’s top professional players.
  • 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_69bd43cf363c819087fd5ab441b4a3f4 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd59c11f5481909f61e23503711cf5 completed March 20, 2026, 2:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdfa895b7481909e54cfa56a54c8dc completed March 21, 2026, 1:55 a.m.
Created at: March 20, 2026, 1:12 p.m.