Triple
T17569568
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tobias Koppers |
E427899
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Tobias Koppers |
—
|
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: Tobias Koppers | Statement: [Tobias Koppers, name, Tobias Koppers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tobias Koppers Context triple: [Tobias Koppers, name, Tobias Koppers]
-
A.
Tobias Koppers
chosen
Tobias Koppers is a German software engineer best known for creating Webpack, a widely used JavaScript module bundler in modern web development.
-
B.
Jens Meurer
Jens Meurer is a German film producer and documentary filmmaker known for his work on international arthouse and historical dramas.
-
C.
Markus Oberhumer
Markus Oberhumer is an Austrian software developer best known as the creator of the UPX executable packer and contributor to various open-source compression and optimization tools.
-
D.
Michael Grunst
Michael Grunst is a German local politician who serves as the borough mayor of Berlin’s Lichtenberg district.
-
E.
Tobias Kohn
Tobias Kohn is a computer scientist and software developer known for his contributions to the Python language, including co-authoring PEP 622 on pattern matching.
- 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_69d889e0385081908a04b66f4dd4bd0d |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4592fe8408190bd8fed1920ab3601 |
completed | April 19, 2026, 4:25 a.m. |
Created at: April 10, 2026, 5:50 a.m.