Triple
T36333079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1983 European Cup Final |
E894704
|
entity |
| Predicate | Juventus European Cup titlesAfterMatch |
P102425
|
FINISHED |
| Object | 0 |
—
|
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: 0 | Statement: [1983 European Cup Final, Juventus European Cup titlesAfterMatch, 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: Juventus European Cup titlesAfterMatch Context triple: [1983 European Cup Final, Juventus European Cup titlesAfterMatch, 0]
-
A.
JuventusTitleCount
chosen
Indicates the number of titles that Juventus has won.
-
B.
yearOfEuropeanCupWinnersCupTitle
Indicates the specific year in which an entity won the European Cup Winners' Cup title.
-
C.
UEFAChampionsLeagueTitles
Indicates the number of UEFA Champions League titles an entity (typically a football club) has won.
-
D.
numberOfUEFACupTitles
Indicates the number of UEFA Cup titles that an entity has won.
-
E.
numberOfUEFAEuropaConferenceLeagueTitles
Indicates the number of UEFA Europa Conference League titles that an entity has won.
- 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_69f76e4dcf088190a6c3216c209cab52 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7cec454a88190a9f3bbee2b856636 |
completed | May 3, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69f7c8977c288190997a892ec5f756ed |
completed | May 3, 2026, 10:13 p.m. |
Created at: May 3, 2026, 4:09 p.m.