Triple
T32435745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aki basho |
E828859
|
entity |
| Predicate | numberOfAnnualGrandTournaments |
P65013
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [Aki basho, numberOfAnnualGrandTournaments, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAnnualGrandTournaments Context triple: [Aki basho, numberOfAnnualGrandTournaments, 6]
-
A.
numberOfTournaments
Indicates the total count of tournaments associated with or participated in by an entity.
-
B.
GrandSlamTournamentsCount
Indicates the number of Grand Slam tournaments associated with an entity, typically counting how many such major events they have won, played in, or hold.
-
C.
frequencyOfTournament
chosen
Indicates how often a particular tournament takes place within a given time period.
-
D.
worldChampionshipFrequency
Indicates how often a world championship event is held or occurs over a given period.
-
E.
originalTournamentYear
Indicates the year in which the referenced tournament was first held or originally established.
- 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_69f3491bf298819097b610f772d54a6d |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a01b841f5508190832bba00cf795bad |
completed | May 11, 2026, 11:06 a.m. |
| PD | Predicate disambiguation | batch_6a01b59541fc8190a9b13a55327c4aa1 |
completed | May 11, 2026, 10:55 a.m. |
Created at: May 1, 2026, 12:55 a.m.