Triple
T18271272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Premier League fixtures |
E437619
|
entity |
| Predicate | typicalTeamMatchCount |
P34063
|
FINISHED |
| Object | 38 matches per club |
—
|
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: 38 matches per club | Statement: [Premier League fixtures, typicalTeamMatchCount, 38 matches per club]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTeamMatchCount Context triple: [Premier League fixtures, typicalTeamMatchCount, 38 matches per club]
-
A.
typicalNumberOfMatchesPerSeries
Indicates the usual or standard count of matches that are played within a single series.
-
B.
gameCountPerTeam
chosen
Indicates the number of games associated with or played by each team.
-
C.
totalMatchesPlayed
Indicates the total number of matches that have been played by the referenced entity or between the related entities.
-
D.
teamCountPerGame
Indicates the number of teams that participate in a single game or match.
-
E.
matchupFrequency
Indicates how often a particular pair or set of entities are matched or paired against each other within a given context or timeframe.
- 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_69d8b913351c8190932b6a426de04b41 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ff7e00548190a28916a696831336 |
completed | April 19, 2026, 4:14 p.m. |
| PD | Predicate disambiguation | batch_69e44fd81c788190b08c6be3b07a08c5 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:34 a.m.