Triple
T8017799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fils |
E186662
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Wiesensteig |
E710133
|
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: Wiesensteig | Statement: [Fils, flowsThrough, Wiesensteig]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wiesensteig Context triple: [Fils, flowsThrough, Wiesensteig]
-
A.
Wiesensteig
chosen
Wiesensteig is a small historic town in the Swabian Alps region of Baden-Württemberg in southern Germany.
-
B.
Stechelberg
Stechelberg is a small Swiss village at the end of the Lauterbrunnen Valley, known for its dramatic alpine scenery and access to hiking and cable cars into the surrounding mountains.
-
C.
Brennkogel
Brennkogel is a mountain peak in the Austrian Alps, located within the Glockner Group of the High Tauern range.
-
D.
Niederwerrn
Niederwerrn is a municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
-
E.
Altensteig
Altensteig is a small historic town in the Black Forest region of Baden-Württemberg, Germany, known for its medieval old town and picturesque hillside 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_69ca82ac7fc081909b1398cf025423af |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3df4f1b8819089a8b67f136bce9a |
completed | March 31, 2026, 3:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc93b18a6c81908a3a4bc25552d97b |
completed | April 1, 2026, 3:40 a.m. |
Created at: March 30, 2026, 5:20 p.m.