Triple
T6551944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gronau (Westf) |
E151149
|
entity |
| Predicate | river |
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: [Gronau (Westf), river, Dinkel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dinkel Context triple: [Gronau (Westf), river, 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.
Millet
Millet is a common French surname borne by several notable figures, including artists and sculptors.
-
D.
Farino
Farino is a small rural commune in the South Province of New Caledonia, known for its lush forests and eco-tourism activities.
-
E.
Barley
Barley is a small rural village in the North Hertfordshire district of England, known for its historic buildings and traditional English countryside setting.
- 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_69c687f3fd60819083bfa583e5bcfa71 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ae05cd988190a013226b14cd98f0 |
completed | March 27, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d55416e48190b574e37a6f2e6690 |
completed | March 27, 2026, 7:07 p.m. |
Created at: March 27, 2026, 1:51 p.m.