Triple
T13030695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gene Sarazen |
E326430
|
entity |
| Predicate | achievedCareerGrandSlam |
P107536
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Gene Sarazen, achievedCareerGrandSlam, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: achievedCareerGrandSlam Context triple: [Gene Sarazen, achievedCareerGrandSlam, true]
-
A.
grandSlamHitter
Indicates that the subject is a baseball player who has hit at least one grand slam (a home run with the bases loaded).
-
B.
GrandSlams
Indicates that an entity has won or is associated with victories in major Grand Slam tournaments within a given sport.
-
C.
otherGrandSlams
Indicates that an entity is associated with Grand Slam tournaments other than a primary or specifically referenced one.
-
D.
hasWonGrandSlamTitleType
Indicates that an entity has won at least one Grand Slam title of a specified type (e.g., singles, doubles, mixed).
-
E.
grandSlamSinglesTitles
Indicates the number of Grand Slam singles tennis titles an entity has won.
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97efd18308190877b3269403b36e2 |
completed | April 10, 2026, 10:51 p.m. |
| PD | Predicate disambiguation | batch_69d97dc39a0881908119c62e31bf6182 |
completed | April 10, 2026, 10:46 p.m. |
| PDg | Predicate description generation | batch_69d97e3df2288190a7f27d31d248bb7f |
completed | April 10, 2026, 10:48 p.m. |
Created at: April 9, 2026, 8:54 p.m.