Triple
T22468933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A6 motorway |
E555436
|
entity |
| Predicate | connects |
P390
|
FINISHED |
| Object | interchange Spiez |
—
|
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: interchange Spiez | Statement: [A6 motorway, connects, interchange Spiez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: interchange Spiez Context triple: [A6 motorway, connects, interchange Spiez]
-
A.
Spiez
chosen
Spiez is a picturesque Swiss town in the Bernese Oberland, known for its lakeside setting, historic castle, and views of the surrounding Alps.
-
B.
Spoerri
Spoerri is the surname of Daniel Spoerri, a Swiss artist known for his pioneering work in assemblage and the Nouveau Réalisme movement.
-
C.
Spiess
Spiess is a surname of German origin, often borne by individuals in German-speaking countries and their descendants.
-
D.
Sieber
Sieber is the family name of Maria Riva, the actress and author who was the daughter of film legend Marlene Dietrich.
-
E.
Sieber
Sieber is a small river in the German state of Lower Saxony that flows through the Harz Mountains and into the Oder.
- 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_69e11e52c2048190952dc5df209b9bed |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15bdda62c8190937ac13a4481b4b7 |
completed | April 29, 2026, 1:16 a.m. |
Created at: April 16, 2026, 8:48 p.m.