Triple
T1321317
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Masayuki Kakefu |
E28223
|
entity |
| Predicate | hasSportSpecialization |
P1080
|
FINISHED |
| Object | batting |
—
|
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: batting | Statement: [Masayuki Kakefu, hasSportSpecialization, batting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSportSpecialization Context triple: [Masayuki Kakefu, hasSportSpecialization, batting]
-
A.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
B.
includesSport
Indicates that one entity contains, offers, or features a particular sport as part of its activities, content, or composition.
-
C.
hasAthletics
Indicates that an entity participates in, is associated with, or offers athletics-related activities or programs.
-
D.
primarySport
chosen
Indicates the main sport with which an entity (such as a person, team, or organization) is most closely associated or primarily involved.
-
E.
ridingSpecialty
Indicates that one entity has a particular area of expertise or focus related to riding (e.g., a specific riding style, discipline, or type).
- 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_69a498540a2481909e807a762280d3ba |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c19932888190a3d45871e84f112e |
completed | March 1, 2026, 10:45 p.m. |
| PD | Predicate disambiguation | batch_69a4beedb49c8190beb5b85cdda05013 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:55 p.m.