Triple
T2991650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lahn |
E80767
|
entity |
| Predicate | mouthLocation |
P417
|
FINISHED |
| Object | Lahnstein |
E214200
|
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: Lahnstein | Statement: [Lahn, mouthLocation, Lahnstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lahnstein Context triple: [Lahn, mouthLocation, Lahnstein]
-
A.
Lahnstein
chosen
Lahnstein is a historic town in western Germany, located on the Rhine River in the state of Rhineland-Palatinate.
-
B.
Brackenberg
Brackenberg is an early recorded historical name for the Brocken, the highest peak in Germany’s Harz Mountains.
-
C.
Drensteinfurt
Drensteinfurt is a small town in North Rhine-Westphalia, Germany, known for its historic architecture and location in the Münsterland region.
-
D.
Miesbach
Miesbach is a historic town in southern Germany known for its traditional Bavarian culture and picturesque Alpine foothill setting.
-
E.
Lauterach
Lauterach is a small municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, known for its rural character and scenic Swabian Jura surroundings.
- 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_69ad8b16c3488190b47b6aa7a59a335b |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99df69d08190a0e25efb0dc8d653 |
completed | March 8, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b24b079a808190adcbac948ad067e9 |
completed | March 12, 2026, 5:11 a.m. |
Created at: March 8, 2026, 2:59 p.m.