Triple
T11279776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rhein-Sieg-Kreis |
E267032
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | Troisdorf |
E580736
|
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: Troisdorf | Statement: [Rhein-Sieg-Kreis, largestCity, Troisdorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Troisdorf Context triple: [Rhein-Sieg-Kreis, largestCity, Troisdorf]
-
A.
Troisdorf
chosen
Troisdorf is a town in North Rhine-Westphalia, Germany, located between Cologne and Bonn and known as an important industrial and commuter hub in the Rhine-Sieg district.
-
B.
Remscheid
Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
-
C.
Hennef
Hennef is a town in North Rhine-Westphalia, Germany, situated on the river Sieg near Bonn and known for its mix of residential areas, industry, and surrounding countryside.
-
D.
Mülheim an der Ruhr
Mülheim an der Ruhr is a city in western Germany’s Ruhr area, known for its industrial heritage, riverside setting on the Ruhr River, and role as a regional economic and cultural center.
-
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_69d6aac8c2f48190ad0596f1f89f0470 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e969b3448190940e2bd499d2d7de |
completed | April 9, 2026, 6:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f28053fe08819099e848cd74b989a0 |
completed | April 29, 2026, 10:04 p.m. |
Created at: April 8, 2026, 9:31 p.m.