Triple

T3468130
Position Surface form Disambiguated ID Type / Status
Subject Lavazza E73185 entity
Predicate sponsors P1807 FINISHED
Object Roland-Garros 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: Roland-Garros | Statement: [Lavazza, sponsors, Roland-Garros]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Roland-Garros
Context triple: [Lavazza, sponsors, Roland-Garros]
  • 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. Tournefeuille
    Tournefeuille is a suburban town in southwestern France, located near Toulouse in the Occitanie region.
  • 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. Parc des Princes
    Parc des Princes is a major football stadium in Paris, best known as the historic home ground of Paris Saint-Germain (PSG).
  • 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_69ad85b224d481908ff8be51338d24ff completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adbb11ec5881908347bf92883a25ee completed March 8, 2026, 6:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3680763608190acdd146dc7c0b239 completed March 13, 2026, 1:27 a.m.
Created at: March 8, 2026, 3:17 p.m.