Triple
T25462597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rod Laver |
E638089
|
entity |
| Predicate | hasWonFrenchOpenSinglesTitles |
P9290
|
FINISHED |
| Object | 2 |
—
|
LITERAL 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: 2 | Statement: [Rod Laver, hasWonFrenchOpenSinglesTitles, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWonFrenchOpenSinglesTitles Context triple: [Rod Laver, hasWonFrenchOpenSinglesTitles, 2]
-
A.
yearsWonFrenchOpenSingles
Indicates the specific years in which an entity won the French Open singles title.
-
B.
frenchOpenSinglesTitles
chosen
Indicates the number of French Open singles titles an entity has won.
-
C.
grandSlamBestResultFrenchOpen
Indicates the best performance or highest round an entity has achieved specifically at the French Open in Grand Slam competition.
-
D.
grandSlamFinalFrenchOpenYear
Indicates the year in which a specific French Open tennis tournament served as the final (championship) match of a Grand Slam event.
-
E.
numberOfUSOpenChampionshipsWon
Indicates the count of US Open Championship titles that an entity has won.
- F. None of above.
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_69e75db8bab08190baca80b4a8c315fd |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f6a28c7c148190bfc980aad9f678ca |
completed | May 3, 2026, 1:19 a.m. |
| PD | Predicate disambiguation | batch_69f69fe1e3c88190830bb2e9f407357e |
completed | May 3, 2026, 1:07 a.m. |
Created at: April 21, 2026, 2:12 p.m.