Triple
T6715579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roland Garros Airport |
E153258
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Roland Garros |
E297110
|
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: [Roland Garros Airport, namedAfter, Roland Garros]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roland Garros Context triple: [Roland Garros Airport, namedAfter, Roland Garros]
-
A.
Stade Roland Garros
chosen
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.
-
B.
Tournefeuille
Tournefeuille is a suburban town in southwestern France, located near Toulouse in the Occitanie region.
-
C.
French Open
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.
-
D.
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).
-
E.
Billancourt
Billancourt is a Paris Métro station in Boulogne-Billancourt serving the western suburbs of the French capital.
- 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_69c68809b4608190a2509ddb5ab87f05 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d1246b748190aed94e8ab8625f7e |
completed | March 27, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c70096e05c8190abfa90996db37eeb |
completed | March 27, 2026, 10:11 p.m. |
Created at: March 27, 2026, 2:07 p.m.