Triple
T17375933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dino Toppmöller |
E422437
|
entity |
| Predicate | father |
P120
|
FINISHED |
| Object | Klaus Toppmöller |
—
|
NE NERFINISHED |
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: Klaus Toppmöller | Statement: [Dino Toppmöller, father, Klaus Toppmöller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Klaus Toppmöller Context triple: [Dino Toppmöller, father, Klaus Toppmöller]
-
A.
Klaus Toppmöller
chosen
Klaus Toppmöller is a German football manager and former player best known for leading Bayer Leverkusen to the 2002 UEFA Champions League final.
-
B.
Dino Toppmöller
Dino Toppmöller is a German football coach and former player known for managing top-flight clubs in the Bundesliga.
-
C.
Holger Pedersen
Holger Pedersen was a Danish linguist and philologist renowned for his pioneering work in comparative linguistics and early formulation of macro-family language hypotheses.
-
D.
Jens E. Groth
Jens E. Groth is a cryptographer best known for his influential work on succinct non-interactive zero-knowledge proofs and other foundational advances in modern cryptographic protocols.
-
E.
Torsten Persson
Torsten Persson is a prominent Swedish economist known for his influential work in political economics and public finance.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a6c864481908507290282cc6d25 |
completed | April 19, 2026, 2:14 a.m. |
Created at: April 10, 2026, 5:45 a.m.