Triple
T34700684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ryan Garcia |
E1000357
|
entity |
| Predicate | hasAmateurCareer |
P123931
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Ryan Garcia, hasAmateurCareer, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAmateurCareer Context triple: [Ryan Garcia, hasAmateurCareer, yes]
-
A.
wasAmateurOrSemiPro
chosen
Indicates that the subject participated in an activity at an amateur or semi-professional level, rather than as a full professional.
-
B.
amateurCareerRecord
Indicates the win–loss (and possibly draw) record an individual achieved during their amateur-level career in a given activity or sport.
-
C.
hasAmateurLevel
Indicates that an entity possesses an amateur level of skill, experience, or proficiency in a given activity or domain.
-
D.
hasPlayedProfessionalSports
Indicates that an entity has participated as an athlete in an officially recognized professional-level sports competition or league.
-
E.
beganAmateurCareer
Indicates that an entity started its amateur-level career or involvement in a particular field or activity.
- 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_69f76dab937881909c86f1b9ad50445f |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f7805ce6208190ac6dbd9c97989978 |
completed | May 3, 2026, 5:05 p.m. |
| PD | Predicate disambiguation | batch_69f77956ec648190ba4fb7e9d83fd107 |
completed | May 3, 2026, 4:35 p.m. |
Created at: May 3, 2026, 3:59 p.m.