Triple
T32470823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krisztina Egerszegi |
E829842
|
entity |
| Predicate | EuropeanChampionshipsTitleCount |
P13984
|
FINISHED |
| Object | multiple |
—
|
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: multiple | Statement: [Krisztina Egerszegi, EuropeanChampionshipsTitleCount, multiple]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: EuropeanChampionshipsTitleCount Context triple: [Krisztina Egerszegi, EuropeanChampionshipsTitleCount, multiple]
-
A.
EuropeanChampionshipTitles
chosen
Indicates the number of European Championship titles an entity has won.
-
B.
EuropeanCupTitles
Indicates the number of European Cup (now UEFA Champions League) titles that an entity, typically a football club, has won.
-
C.
EuropeanChampionshipAppearances
Indicates the number of times an entity has participated in a European Championship tournament.
-
D.
UEFAChampionsLeagueTitles
Indicates the number of UEFA Champions League titles an entity (typically a football club) has won.
-
E.
EuropeanTrophyCount
Indicates the number of European football trophies a team has won in officially recognized continental competitions.
- 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_69f3491ee87c81908cbf5890079c2af6 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c355c35c819091b633137f54c14a |
completed | May 3, 2026, 3:39 a.m. |
| PD | Predicate disambiguation | batch_69f6ba700a708190ab6db62791e43774 |
completed | May 3, 2026, 3:01 a.m. |
Created at: May 1, 2026, 12:57 a.m.