Triple
T19869802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | B33 road |
E477482
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Konstanz |
—
|
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: Konstanz | Statement: [B33 road, passesThrough, Konstanz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Konstanz Context triple: [B33 road, passesThrough, Konstanz]
-
A.
Konstanz
chosen
Konstanz is a historic city on the shores of Lake Constance in southern Germany, known for its well-preserved medieval old town and role as a regional cultural and economic center.
-
B.
Augsburg
Augsburg is one of Germany’s oldest cities, a historic Bavarian center known for its rich Renaissance heritage and role as a major medieval trading hub.
-
C.
Offenburg
Offenburg is a city in southwestern Germany’s Baden-Württemberg state, known as a regional economic and transport hub near the French border in the Upper Rhine region.
-
D.
Kaufbeuren
Kaufbeuren is a historic Bavarian town in southern Germany known for its well-preserved medieval old town and traditional Swabian culture.
-
E.
Donaueschingen
Donaueschingen is a town in southwestern Germany, in the Black Forest region of Baden-Württemberg, known as one of the sources of the Danube River.
- 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_69d8e51e7d948190aedbcd6c30361c39 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e658a2cc8481908d134b0b5cf79d06 |
completed | April 20, 2026, 4:47 p.m. |
Created at: April 10, 2026, 1:51 p.m.