Triple
T22468948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A6 motorway |
E555436
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Münsingen |
—
|
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: Münsingen | Statement: [A6 motorway, passesNear, Münsingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Münsingen Context triple: [A6 motorway, passesNear, Münsingen]
-
A.
Münsingen
chosen
Münsingen is a Swiss municipality in the canton of Bern, known for its scenic location in the Aare valley between Bern and Thun.
-
B.
Menzingen
Menzingen is a municipality in the canton of Zug in central Switzerland, known for its rural landscape and location in the pre-Alpine region.
-
C.
Mötzingen
Mötzingen is a small municipality in the German state of Baden-Württemberg, located in the Böblingen region of southwestern Germany.
-
D.
Memmingen
Memmingen is a historic town in the Bavarian region of Germany, known for its well-preserved medieval old town and role as a regional transport hub.
-
E.
Miesbach
Miesbach is a historic town in southern Germany known for its traditional Bavarian culture and picturesque Alpine foothill setting.
- 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.