Triple
T5930992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carlo Ancelotti |
E131935
|
entity |
| Predicate | UEFAChampionsLeagueTitlesAsPlayer |
P40880
|
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: [Carlo Ancelotti, UEFAChampionsLeagueTitlesAsPlayer, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: UEFAChampionsLeagueTitlesAsPlayer Context triple: [Carlo Ancelotti, UEFAChampionsLeagueTitlesAsPlayer, 2]
-
A.
wonChampionsLeagueAsPlayer
chosen
Indicates that the subject has been part of a team that won the UEFA Champions League in the role of a player.
-
B.
UEFAChampionsLeagueTitles
Indicates the number of UEFA Champions League titles an entity (typically a football club) has won.
-
C.
UEFAEuropaLeagueTitles
Indicates the number of UEFA Europa League championship titles an entity has won.
-
D.
numberOfTropheeDesChampionsTitles
Indicates the number of Trophée des Champions titles that an entity has won.
-
E.
EuropeanChampionshipAppearances
Indicates the number of times an entity has participated in a European Championship tournament.
- 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_69c0085b75e88190a632f9691f9da48b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03c9239e08190bff7ef2bd6d21ae0 |
completed | March 22, 2026, 7:01 p.m. |
| PD | Predicate disambiguation | batch_69c033541d108190a34d1fde2fe9dacb |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 4 p.m.