Triple
T5132589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruhr area |
E115735
|
entity |
| Predicate | hasRiver |
P165
|
FINISHED |
| Object | Ruhr |
E80553
|
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: Ruhr | Statement: [Ruhr area, hasRiver, Ruhr]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ruhr Context triple: [Ruhr area, hasRiver, Ruhr]
-
A.
Ruhr
chosen
The Ruhr is a river in western Germany that flows through the Ruhr industrial region before joining the Rhine.
-
B.
North Rhine
North Rhine is a historical region in western Germany that forms part of the larger Rhineland area along the Rhine River.
-
C.
Rhein II
Rhein II is a large-scale color photograph by German visual artist Andreas Gursky, renowned for its minimalist depiction of the Rhine River and for once being the most expensive photograph ever sold at auction.
-
D.
Roer
The Roer is a river in Western Europe that flows through parts of Belgium, Germany, and the Netherlands before joining the Meuse.
-
E.
Lippe
Lippe is a historical region in northwestern Germany that once formed a small principality and later a Free State within the German Reich.
- 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_69bd444426bc819099ccd23f141e22aa |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd784b477c8190926daddb28a255af |
completed | March 20, 2026, 4:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf70c59678819097aed5af25f36b66 |
completed | March 22, 2026, 4:32 a.m. |
Created at: March 20, 2026, 1:42 p.m.