Triple
T15870497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UEFA Champions League quarter-finals |
E384816
|
entity |
| Predicate | matchCountPerSeason |
P12655
|
FINISHED |
| Object | 8 matches in main format |
—
|
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: 8 matches in main format | Statement: [UEFA Champions League quarter-finals, matchCountPerSeason, 8 matches in main format]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: matchCountPerSeason Context triple: [UEFA Champions League quarter-finals, matchCountPerSeason, 8 matches in main format]
-
A.
totalMatchesPerSeason
Indicates the total number of matches associated with an entity within a single season.
-
B.
tournamentWinsInSeason
Indicates that one entity (typically a team or player) has achieved a specified number of tournament victories during a particular season.
-
C.
seasonSeriesPlayedPerYear
Indicates the number of series played in a season during a given year.
-
D.
typicalNumberOfMeetingsPerSeason
chosen
Indicates the usual or average count of meetings that occur within a single season.
-
E.
seasonCountDetail
Indicates the specific number of seasons associated with something, often including additional contextual details about that season count.
- 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_69d86da4e86481909f1325fdc971b5ec |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e174de2cd48190ab18e48c9f051a2a |
completed | April 16, 2026, 11:46 p.m. |
| PD | Predicate disambiguation | batch_69e142b976c081908d3ba3e705419f3a |
completed | April 16, 2026, 8:12 p.m. |
Created at: April 10, 2026, 4:50 a.m.