Triple
T25462598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rod Laver |
E638089
|
entity |
| Predicate | hasWonUSOpenSinglesTitles |
P57439
|
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, hasWonUSOpenSinglesTitles, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWonUSOpenSinglesTitles Context triple: [Rod Laver, hasWonUSOpenSinglesTitles, 2]
-
A.
numberOfUSOpenChampionshipsWon
chosen
Indicates the count of US Open Championship titles that an entity has won.
-
B.
wonUSOpen
Indicates that one entity achieved victory in the US Open competition or tournament over another entity or in a given year.
-
C.
yearsWonFrenchOpenSingles
Indicates the specific years in which an entity won the French Open singles title.
-
D.
grandSlamSinglesTitles
Indicates the number of Grand Slam singles tennis titles an entity has won.
-
E.
grandSlamBestResultUSOpen
Indicates the best performance or highest round an entity has achieved specifically at the US Open tennis Grand Slam tournament.
- 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_69f6a8df16a88190a23820e64a3b1f92 |
completed | May 3, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69f6a751d5e48190a77dcecbe7ef9f0b |
completed | May 3, 2026, 1:39 a.m. |
Created at: April 21, 2026, 2:12 p.m.