Triple
T10126808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Domscheit-Berg |
E226235
|
entity |
| Predicate | birthPlace |
P1
|
FINISHED |
| Object | Bergisch Gladbach |
E584520
|
NE 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: Bergisch Gladbach | Statement: [Daniel Domscheit-Berg, birthPlace, Bergisch Gladbach]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bergisch Gladbach Context triple: [Daniel Domscheit-Berg, birthPlace, Bergisch Gladbach]
-
A.
Bergisch Gladbach
chosen
Bergisch Gladbach is a city in North Rhine-Westphalia, western Germany, known for its paper industry, proximity to Cologne, and surrounding Bergisches Land countryside.
-
B.
Mönchengladbach
Mönchengladbach is a city in western Germany known for its textile industry heritage and its football club Borussia Mönchengladbach.
-
C.
Remscheid
Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
-
D.
Gelsenkirchen
Gelsenkirchen is a city in western Germany known for its strong football culture and modern stadium, Veltins-Arena, home to FC Schalke 04.
-
E.
Krefeld
Krefeld is a city in western Germany near the Rhine River, known historically for its textile and silk industry.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca843057b48190a86730167f5d6b98 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cdd2eef7388190b95ffd02814f2d1f |
completed | April 2, 2026, 2:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4f31e3d5c8190a044eaf67ebc9f08 |
completed | April 19, 2026, 3:22 p.m. |
Created at: March 30, 2026, 9:05 p.m.