Triple
T11949198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1985 European Cup Final |
E284383
|
entity |
| Predicate | JuventusTitleCount |
P102425
|
FINISHED |
| Object | 1st European Cup title |
—
|
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: 1st European Cup title | Statement: [1985 European Cup Final, JuventusTitleCount, 1st European Cup title]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: JuventusTitleCount Context triple: [1985 European Cup Final, JuventusTitleCount, 1st European Cup title]
-
A.
numberOfCoppaItaliaTitles
Indicates the quantity of Coppa Italia titles that an entity has won.
-
B.
numberOfSupercoppaItalianaTitles
Indicates the count of Supercoppa Italiana titles that an entity has won.
-
C.
numberOfTropheeDesChampionsTitles
Indicates the number of Trophée des Champions titles that an entity has won.
-
D.
mostChampionshipTitlesClub
Indicates that a club holds the highest number of championship titles within a given competition or context.
-
E.
numberOfCoupeDeLaLigueTitles
Indicates the total count of Coupe de la Ligue titles that an entity has won.
- F. None of above. chosen
Provenance (4 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90346825c8190ab4482a1fc8eed56 |
completed | April 10, 2026, 2:03 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3e48e08190b2fee43af4f57323 |
completed | April 10, 2026, 8:56 a.m. |
| PDg | Predicate description generation | batch_69d8dd0ba0f88190b7d5e358c27ca184 |
completed | April 10, 2026, 11:20 a.m. |
Created at: April 8, 2026, 9:45 p.m.