Triple
T24587586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chris Evert |
E608438
|
entity |
| Predicate | clayCourtWinningPercentage |
P156458
|
FINISHED |
| Object | over 90 percent |
—
|
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: over 90 percent | Statement: [Chris Evert, clayCourtWinningPercentage, over 90 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: clayCourtWinningPercentage Context triple: [Chris Evert, clayCourtWinningPercentage, over 90 percent]
-
A.
centreCourtName
Indicates the name assigned to the central court associated with a particular venue or event.
-
B.
numberOfUSOpenChampionshipsWon
Indicates the count of US Open Championship titles that an entity has won.
-
C.
primaryLocationOnCourt
Indicates the main area or position on the court where an entity is typically located or operates.
-
D.
positionInTennis
Indicates the specific role or court location a player occupies during a tennis match or point.
-
E.
helpedWinGrandSlamsWith
Indicates that one entity significantly contributed to another entity’s victories in Grand Slam tournaments.
- F. None of above. chosen
Provenance (4 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_69e2c4ce89248190ad99e18f0638dfbb |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a988393c81909f0292e9b35441d2 |
completed | April 30, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69f2a6c1f07081908edf0b521767e79b |
completed | April 30, 2026, 12:48 a.m. |
| PDg | Predicate description generation | batch_69f2a846c5bc81909ba50cee483bea91 |
completed | April 30, 2026, 12:54 a.m. |
Created at: April 18, 2026, 2:29 a.m.