Triple
T7411723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fuhse |
E171019
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Peine |
E345903
|
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: Peine | Statement: [Fuhse, flowsThrough, Peine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peine Context triple: [Fuhse, flowsThrough, Peine]
-
A.
Peine
chosen
Peine is a town in Lower Saxony, Germany, known for its industrial history and location between Hanover and Brunswick.
-
B.
Inn River
The Inn River is a major Alpine river in Central Europe that flows through Switzerland, Austria, and Germany before joining the Danube.
-
C.
Werre
The Werre is a river in North Rhine-Westphalia, Germany, that flows through towns such as Detmold and Herford before joining the Weser.
-
D.
Peene River
The Peene River is a lowland river in northeastern Germany, often called the "Amazon of the North" for its largely untouched wetlands and rich biodiversity.
-
E.
Nahe
Nahe is a renowned German wine region, particularly celebrated for producing high-quality Riesling wines with diverse styles due to its varied soils and microclimates.
- 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_69c68a618bdc81908d8018edadecd1a4 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f2a027148190bdb6a7940389e377 |
completed | March 27, 2026, 9:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c82779a1d8819098f136291fa42433 |
completed | March 28, 2026, 7:09 p.m. |
Created at: March 27, 2026, 3:11 p.m.