Triple
T8147734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hansi Flick |
E190255
|
entity |
| Predicate | leagueTitlesWonWithBayernMunich |
P80306
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Hansi Flick, leagueTitlesWonWithBayernMunich, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leagueTitlesWonWithBayernMunich Context triple: [Hansi Flick, leagueTitlesWonWithBayernMunich, 2]
-
A.
wonBundesligaWith
chosen
Indicates that one entity achieved victory in the Bundesliga while being associated with (e.g., playing for or coaching) the other entity.
-
B.
leagueTitlesWonWithRealMadrid
Indicates the number of league titles an individual has won while playing for or managing Real Madrid.
-
C.
mostChampionshipTitlesClub
Indicates that a club holds the highest number of championship titles within a given competition or context.
-
D.
leagueTitlesWithBarcelonaAsManager
Indicates the number of league titles a person has won while serving as the manager of Barcelona.
-
E.
numberOfTropheeDesChampionsTitles
Indicates the number of Trophée des Champions 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_69ca82be7ba8819087de0147e9292c83 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb447e74e081908df774edb2134209 |
completed | March 31, 2026, 3:50 a.m. |
| PD | Predicate disambiguation | batch_69cb36a0847c8190af9038aef78319b3 |
completed | March 31, 2026, 2:51 a.m. |
Created at: March 30, 2026, 5:36 p.m.