Triple
T19578940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Klaus Schwarzkopf |
E489933
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Klaus Schwarzkopf |
—
|
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 Schwarzkopf | Statement: [Klaus Schwarzkopf, name, Klaus Schwarzkopf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Klaus Schwarzkopf Context triple: [Klaus Schwarzkopf, name, Klaus Schwarzkopf]
-
A.
Klaus Schwarzkopf
chosen
Klaus Schwarzkopf was a German actor best known for his roles in film and television during the mid-20th century.
-
B.
Hans Schwarzkopf
Hans Schwarzkopf was a German chemist and entrepreneur best known for founding the Schwarzkopf hair care brand and pioneering modern shampoo products.
-
C.
Rudolf Schwarzkopf
Rudolf Schwarzkopf is a notable individual who carries the German surname Schwarzkopf, associated with various prominent figures in fields such as music, military, and industry.
-
D.
Winfried Schäfer
Winfried Schäfer is a German football manager best known for his successful stints coaching national teams, including leading Cameroon to victory in the 2002 Africa Cup of Nations.
-
E.
Jürgen Knieper
Jürgen Knieper is a German composer best known for his film and television scores, including work on notable German productions and international art-house films.
- 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_69d8e8dd9374819098e36349b3211663 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6402693d88190a828c0e136895783 |
completed | April 20, 2026, 3:03 p.m. |
Created at: April 10, 2026, 1:42 p.m.