Triple
T23802170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tomas Berdych |
E588701
|
entity |
| Predicate | australianOpenBestResult |
P49843
|
FINISHED |
| Object | semifinalist |
—
|
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: semifinalist | Statement: [Tomas Berdych, australianOpenBestResult, semifinalist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: australianOpenBestResult Context triple: [Tomas Berdych, australianOpenBestResult, semifinalist]
-
A.
grandSlamBestResultAustralianOpen
chosen
Indicates the best performance or highest round an entity has achieved specifically at the Australian Open in Grand Slam competition.
-
B.
grandSlamBestResultUSOpen
Indicates the best performance or highest round an entity has achieved specifically at the US Open tennis Grand Slam tournament.
-
C.
grandSlamBestResultWimbledon
Indicates the best performance or highest round an entity has achieved at the Wimbledon tennis championships.
-
D.
australianOpenSinglesTitles
Indicates the number of Australian Open singles titles one entity has won.
-
E.
wonAustralianOpenSinglesYear
Indicates that the subject won the Australian Open singles tennis title in the specified year.
- 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_69e25d15db58819092ac1e6791696fd9 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1c74e430481909debd10c71785912 |
completed | April 29, 2026, 8:54 a.m. |
| PD | Predicate disambiguation | batch_69f155fe300481909bd617443228df65 |
completed | April 29, 2026, 12:51 a.m. |
Created at: April 17, 2026, 7:53 p.m.