Triple
T18581258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faulhorn |
E454118
|
entity |
| Predicate | nearestTown |
P350
|
FINISHED |
| Object | Brienz |
—
|
NE NERFINISHED |
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: Brienz | Statement: [Faulhorn, nearestTown, Brienz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brienz Context triple: [Faulhorn, nearestTown, Brienz]
-
A.
Brienz
chosen
Brienz is a picturesque Swiss village in the Bernese Oberland, known for its lakeside setting on Lake Brienz and its traditional woodcarving craftsmanship.
-
B.
Sihlwald
Sihlwald is a large forest and nature reserve near Zurich, Switzerland, known for its protected, near-natural woodland and recreational hiking trails.
-
C.
Thun
Thun is a historic Swiss town in the canton of Bern, known for its medieval old town, lakeside setting on Lake Thun, and views of the surrounding Alps.
-
D.
Weggis
Weggis is a picturesque lakeside resort town in central Switzerland, known for its mild climate, scenic views of the Alps, and tourism on the shores of Lake Lucerne.
-
E.
Thun–Interlaken West
Thun–Interlaken West is a scenic passenger route in Switzerland connecting the town of Thun with Interlaken’s western railway station along Lake Thun.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d38974308190a9174430ef256b73 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e543cffd34819090d636fd584f51db |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 10, 2026, 11:43 a.m.