Triple
T4406379
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kandel |
E93741
|
entity |
| Predicate | hasScenicView |
P9193
|
FINISHED |
| Object | Feldberg |
E89914
|
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: Feldberg | Statement: [Kandel, hasScenicView, Feldberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Feldberg Context triple: [Kandel, hasScenicView, Feldberg]
-
A.
Feldberg
chosen
Feldberg is the tallest mountain in Germany’s Black Forest region, known for its scenic landscapes and popular hiking and skiing opportunities.
-
B.
Erzhausen
Erzhausen is a small municipality in the state of Hesse in central Germany, located near Darmstadt and part of the Rhine-Main metropolitan region.
-
C.
Großer Feldberg
Großer Feldberg is a prominent mountain in Hesse, Germany, known for its scenic views, hiking trails, and telecommunications facilities.
-
D.
Hornsberg
Hornsberg is a waterfront residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
-
E.
Todtnau
Todtnau is a small town in Germany’s Black Forest region, known for its mountainous scenery, outdoor recreation, and proximity to the Feldberg peak.
- 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_69b345158c748190a2c040fce2da9980 |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b35489f6948190a4b2c259f64b4abf |
completed | March 13, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5f6058f2c8190a50862d1a4607f3c |
completed | March 14, 2026, 11:57 p.m. |
Created at: March 12, 2026, 11:28 p.m.