Triple
T20721007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Museum Barberini |
E509311
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object | Hasso Plattner |
—
|
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: Hasso Plattner | Statement: [Museum Barberini, foundedBy, Hasso Plattner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hasso Plattner Context triple: [Museum Barberini, foundedBy, Hasso Plattner]
-
A.
Hasso Plattner
chosen
Hasso Plattner is a German billionaire entrepreneur and philanthropist best known as a co-founder of the enterprise software giant SAP and a prominent patron of science and the arts.
-
B.
Klaus Tschira
Klaus Tschira was a German physicist, entrepreneur, and philanthropist best known as one of the co-founders of the software company SAP.
-
C.
Dietmar Hopp
Dietmar Hopp is a German billionaire businessman and philanthropist best known as a co-founder of the software company SAP and a major patron of sports and medical research.
-
D.
Georg Siemens
Georg Siemens was a German banker and entrepreneur best known as a co-founder and early leader of Deutsche Bank, helping shape it into a major international financial institution.
-
E.
Gerhard Weikum
Gerhard Weikum is a prominent German computer scientist known for his influential research in database systems, information retrieval, and knowledge bases.
- 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_69e0b4c4cc648190b45fda6e2b20af56 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c1d5eec08190832ffba8a90f3076 |
completed | April 21, 2026, 12:16 a.m. |
Created at: April 16, 2026, 12:27 p.m.