Triple
T24323606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UEFA Champions League Final 2020 |
E613035
|
entity |
| Predicate | bayernMatchesWonInCampaign |
P155724
|
FINISHED |
| Object | 11 |
—
|
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: 11 | Statement: [UEFA Champions League Final 2020, bayernMatchesWonInCampaign, 11]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bayernMatchesWonInCampaign Context triple: [UEFA Champions League Final 2020, bayernMatchesWonInCampaign, 11]
-
A.
bayernTitleCount
Indicates the number of titles that Bayern Munich has won.
-
B.
wonBundesligaWith
Indicates that one entity achieved victory in the Bundesliga while being associated with (e.g., playing for or coaching) the other entity.
-
C.
numberOfBundesligaTitles
Indicates the quantity of Bundesliga championship titles that an entity has won.
-
D.
numberOfGermanSupercups
Indicates the total count of German Supercup titles associated with a given team or entity.
-
E.
numberOfGermanChampionships
Indicates the count of German championship titles associated with a given entity.
- 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_69e2d7db6d5c819091194918157a7c1f |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f292ae127881909ef1772278181ce3 |
completed | April 29, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69f287ad30048190b3ad3613486f277f |
completed | April 29, 2026, 10:35 p.m. |
| PDg | Predicate description generation | batch_69f28b7ff1808190870dfe9af789a1eb |
completed | April 29, 2026, 10:51 p.m. |
Created at: April 18, 2026, 1:53 a.m.