Triple
T27455647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Klaus Fischer |
E692583
|
entity |
| Predicate | rankingByGoalsInBundesliga |
P167269
|
FINISHED |
| Object | one of the all-time top scorers |
—
|
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: one of the all-time top scorers | Statement: [Klaus Fischer, rankingByGoalsInBundesliga, one of the all-time top scorers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingByGoalsInBundesliga Context triple: [Klaus Fischer, rankingByGoalsInBundesliga, one of the all-time top scorers]
-
A.
seasonOfBestBundesligaFinish
Indicates the specific season in which an entity achieved its highest-ever finishing position in the Bundesliga.
-
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.
goalsForBayernMunich
Indicates the number of goals that were scored in favor of Bayern Munich in a given match or context.
-
D.
numberOfBundesligaTitles
Indicates the quantity of Bundesliga championship titles that an entity has won.
-
E.
rankingAmongScorers
chosen
Indicates the relative position or rank of an entity compared to others based on the number of points or scores they have achieved.
- 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_69ef5207903881909427745cda05d27a |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f6978fe97081908fe568091ad9b159 |
completed | May 3, 2026, 12:32 a.m. |
| PD | Predicate disambiguation | batch_69f69661e6ec8190948251c7516a32ad |
completed | May 3, 2026, 12:27 a.m. |
Created at: April 27, 2026, 12:48 p.m.