Triple
T17053033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Losser |
E413748
|
entity |
| Predicate | traversedByRiver |
P165
|
FINISHED |
| Object | Dinkel |
E413754
|
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: Dinkel | Statement: [Losser, traversedByRiver, Dinkel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dinkel Context triple: [Losser, traversedByRiver, Dinkel]
-
A.
Dinkel
chosen
Dinkel is a small river in the eastern Netherlands and western Germany, known for flowing through the Twente region and its relatively unspoiled natural landscapes.
-
B.
Emmer
Emmer is a river in northwestern Germany that flows through Lower Saxony and North Rhine-Westphalia before joining the Weser.
-
C.
Centeno
Centeno is a Portuguese surname most notably associated with Mário Centeno, an economist and former finance minister of Portugal.
-
D.
Millet
Millet is a group of small-seeded cereal grains widely cultivated for food and fodder, especially in semi-arid regions due to their drought resistance and short growing season.
-
E.
Millet
Millet is a common French surname borne by several notable figures, including artists and sculptors.
- 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_69d886cde3d481908d4d01ba88ba7eb7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3daa491008190ad013ee37532aa51 |
completed | April 18, 2026, 7:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a012343eca0819086a07511c5d22878 |
completed | May 11, 2026, 12:31 a.m. |
Created at: April 10, 2026, 5:34 a.m.