Triple
T1848345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1. FC Nürnberg |
E41334
|
entity |
| Predicate | numberOfGermanChampionships |
P34121
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [1. FC Nürnberg, numberOfGermanChampionships, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfGermanChampionships Context triple: [1. FC Nürnberg, numberOfGermanChampionships, 9]
-
A.
EuropeanChampionshipTitles
Indicates the number of European Championship titles an entity has won.
-
B.
EuropeanChampionshipAppearances
Indicates the number of times an entity has participated in a European Championship tournament.
-
C.
wonEuropeanChampionship
Indicates that an entity achieved first place or overall victory in a European Championship competition.
-
D.
WorldChampionshipAppearances
Indicates the number of times an entity has participated in a world championship competition.
-
E.
EuropeanCupTitles
Indicates the number of European Cup (now UEFA Champions League) titles that an entity, typically a football club, 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_69a88648cd44819093303206d96d76ad |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb32d35508190bf1c487dffbecaf0 |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abafdca6d8819083c66f3a29fd9fd1 |
completed | March 7, 2026, 4:55 a.m. |
| PDg | Predicate description generation | batch_69abb32a8d548190a231c7c2ce276a5e |
completed | March 7, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:33 p.m.