Triple
T30020691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shizuka Arakawa |
E762727
|
entity |
| Predicate | turned professional |
P27330
|
FINISHED |
| Object | 2006 |
—
|
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: 2006 | Statement: [Shizuka Arakawa, turned professional, 2006]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: turned professional Context triple: [Shizuka Arakawa, turned professional, 2006]
-
A.
transitionedToProfessionalStatusAs
Indicates that an entity changed from a non-professional state or role into a professional status specifically in relation to another entity.
-
B.
turnedProfessionalInBodybuilding
Indicates that a person began their career as a professional in the sport of bodybuilding at a specified time or event.
-
C.
turnedPro
Indicates that an individual transitioned from amateur status to professional status in a particular field or activity.
-
D.
professionalSince
chosen
Indicates the point in time when an entity began its professional activity or career in a given role or field.
-
E.
professionalWins
Indicates that one entity has achieved a certain number of victories or successes in a professional context, such as in a career, competition, or formal domain.
- 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_69f2246ee6e48190b69e837b913b398a |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f67987d8548190ad2276a4bc4c7a10 |
completed | May 2, 2026, 10:24 p.m. |
| PD | Predicate disambiguation | batch_69f66ec9919881908a187bfc7c4df192 |
completed | May 2, 2026, 9:38 p.m. |
Created at: April 29, 2026, 6:47 p.m.