Triple
T17408134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brienz |
E423273
|
entity |
| Predicate | connectedByRailTo |
P848
|
FINISHED |
| Object | Meiringen |
—
|
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: Meiringen | Statement: [Brienz, connectedByRailTo, Meiringen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meiringen Context triple: [Brienz, connectedByRailTo, Meiringen]
-
A.
Meiringen
chosen
Meiringen is a Swiss alpine town in the Bernese Oberland, known for its dramatic mountain scenery, Reichenbach Falls, and association with Sherlock Holmes.
-
B.
Orscheid
Orscheid is a small subdistrict within the Aegidienberg area of Bad Honnef in North Rhine-Westphalia, Germany.
-
C.
Gimmelwald
Gimmelwald is a small, traditional Swiss alpine village known for its dramatic mountain scenery and tranquil, car-free atmosphere in the Bernese Oberland.
-
D.
Kiental
Kiental is a picturesque alpine valley and village in the Bernese Oberland region of Switzerland, known for its dramatic mountain scenery and hiking opportunities.
-
E.
Tannheim
Tannheim is a small municipality in the district of Biberach in the German state of Baden-Württemberg, known for its rural character and Swabian cultural heritage.
- 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_69d889d7d27c819088486ce3f0627fa1 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43b08d5a881909e7a5b2ae2c60898 |
completed | April 19, 2026, 2:16 a.m. |
Created at: April 10, 2026, 5:46 a.m.