Triple
T4990790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wetterstein Mountains |
E112123
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Höllental |
E210797
|
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: Höllental | Statement: [Wetterstein Mountains, contains, Höllental]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Höllental Context triple: [Wetterstein Mountains, contains, Höllental]
-
A.
Höllental
chosen
Höllental is a steep, scenic alpine valley in the Bavarian Alps known for its challenging route up to Germany’s highest peak, the Zugspitze.
-
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.
Teufelssee
Teufelssee is a small natural lake in Berlin known for its scenic setting, recreational swimming, and clothing-optional bathing area.
-
D.
Schönbuch
Schönbuch is a large forest and nature reserve in the German state of Baden-Württemberg, known for its extensive woodlands, wildlife, and recreational hiking areas.
-
E.
Bad Wiessee
Bad Wiessee is a Bavarian spa town in southern Germany, known for its therapeutic iodine-sulfur springs and scenic location on the shores of Lake Tegernsee.
- 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_69bd441be7bc8190b530362d427b97d2 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd728141d48190a0713e6d33c50fb6 |
completed | March 20, 2026, 4:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be8a282dd08190a29b92e8e825a3bb |
completed | March 21, 2026, 12:08 p.m. |
Created at: March 20, 2026, 1:34 p.m.