Triple
T28907143
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FINA Swimming World Cup events |
E733112
|
entity |
| Predicate | typicalNumberOfMeetsPerSeason |
P12655
|
FINISHED |
| Object | 3 to 7 |
—
|
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: 3 to 7 | Statement: [FINA Swimming World Cup events, typicalNumberOfMeetsPerSeason, 3 to 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalNumberOfMeetsPerSeason Context triple: [FINA Swimming World Cup events, typicalNumberOfMeetsPerSeason, 3 to 7]
-
A.
typicalNumberOfMeetingsPerSeason
chosen
Indicates the usual or average count of meetings that occur within a single season.
-
B.
typicalSeasonMeetings
Indicates that there are regularly occurring meetings associated with a particular season or time period.
-
C.
hasRegularSeasonMeetings
Indicates that one entity holds recurring scheduled meetings with another entity during the regular season.
-
D.
typicalNumberOfStopsPerSeason
Indicates the usual or average count of stops that occur in a single season.
-
E.
hasSeasonalParticipationIn
Indicates that an entity takes part in or is involved with another entity only during specific seasons or recurring seasonal periods.
- 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_69f05b096d208190958a57d2e4b5a93a |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_6a01378bf05c8190af9f5e06a2810c7d |
completed | May 11, 2026, 1:57 a.m. |
| PD | Predicate disambiguation | batch_6a0137277c6c8190bcec341f2a0757c4 |
completed | May 11, 2026, 1:55 a.m. |
Created at: April 28, 2026, 8:08 a.m.