Triple
T21279060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gera–Saalfeld railway |
E524468
|
entity |
| Predicate | regionServed |
P82
|
FINISHED |
| Object | Saalfeld urban area |
—
|
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: Saalfeld urban area | Statement: [Gera–Saalfeld railway, regionServed, Saalfeld urban area]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saalfeld urban area Context triple: [Gera–Saalfeld railway, regionServed, Saalfeld urban area]
-
A.
Saalfeld
chosen
Saalfeld is a town in the German state of Thuringia, known for its historic old town and former significance as a regional railway and industrial center.
-
B.
Stuttgart micropolitan area
The Stuttgart micropolitan area is a small U.S. population center in eastern Arkansas anchored by the city of Stuttgart and its surrounding rural communities.
-
C.
Suhl
Suhl is a city in central Germany known historically as a center of firearms manufacturing and located in the federal state of Thuringia.
-
D.
Ebermannstadt
Ebermannstadt is a small historic town in northern Bavaria, Germany, known as a gateway to the scenic Franconian Switzerland region.
-
E.
Stadt Fürth
Stadt Fürth is a Bavarian city in Germany known for its rich Franconian cultural traditions, historic architecture, and vibrant local festivals.
- 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_69e0b516293c819089458ea2ec85f85e |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e73659b56c8190af283348378b1cff |
completed | April 21, 2026, 8:33 a.m. |
Created at: April 16, 2026, 4:02 p.m.