Triple
T10968529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waldeck-Frankenberg |
E259169
|
entity |
| Predicate | hasRiver |
P165
|
FINISHED |
| Object | Diemel |
E255721
|
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: Diemel | Statement: [Waldeck-Frankenberg, hasRiver, Diemel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diemel Context triple: [Waldeck-Frankenberg, hasRiver, Diemel]
-
A.
Diemel
chosen
The Diemel is a river in central Germany that flows through Hesse and North Rhine-Westphalia before joining the Weser.
-
B.
Rhens
Rhens is a historic town on the Rhine River in western Germany, known for its medieval role as a meeting place of the prince-electors of the Holy Roman Empire.
-
C.
Rheine
Rheine is a German city in the state of North Rhine-Westphalia, known for its historical town center and location along the River Ems.
-
D.
Dommel
The Dommel is a river in the southern Netherlands and northern Belgium that flows through cities including Eindhoven before joining the Dieze.
-
E.
Meuse
Meuse is a department in northeastern France known for its rural landscapes and significant World War I battlefields, including Verdun.
- 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_69d6aa895f4c8190887a15460ef622f4 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7719800388190943a0bffa48a2731 |
completed | April 9, 2026, 9:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f12f646ec88190ab4745c52798b599 |
completed | April 28, 2026, 10:06 p.m. |
Created at: April 8, 2026, 9:24 p.m.