Triple
T7922283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Landwasser |
E183972
|
entity |
| Predicate | hasValley |
P650
|
FINISHED |
| Object | Landwassertal |
E712090
|
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: Landwassertal | Statement: [Landwasser, hasValley, Landwassertal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Landwassertal Context triple: [Landwasser, hasValley, Landwassertal]
-
A.
Landwassertal
chosen
Landwassertal is a valley in the Swiss canton of Graubünden, known for its dramatic alpine landscape and the famous Landwasser Viaduct of the Rhaetian Railway.
-
B.
Löstertal
Löstertal is a locality within the town of Wadern in the Saarland region of Germany, known for its rural character and scenic surroundings.
-
C.
Sihltal
Sihltal is a Swiss valley in the canton of Zurich shaped by the Sihl River, known for its scenic landscapes and proximity to the city of Zurich.
-
D.
Münstertal
Münstertal is a picturesque municipality in Germany’s Black Forest region, known for its scenic valley landscapes and traditional rural character.
-
E.
Schuttertal
Schuttertal is a rural municipality in southwestern Germany’s Baden-Württemberg region, situated within the Ortenaukreis district and known for its scenic valleys and Black Forest landscapes.
- 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_69ca828efbe48190bd48482650182e79 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a9499cc8190b6bd81f4625c77ab |
completed | March 31, 2026, 3:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbde69b608190a49d93c04c46787d |
completed | April 1, 2026, 6:40 a.m. |
Created at: March 30, 2026, 5:06 p.m.