Triple
T20481970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stuttgart Schwabstraße |
E502477
|
entity |
| Predicate | hasService |
P182
|
FINISHED |
| Object | S3 |
—
|
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: S3 | Statement: [Stuttgart Schwabstraße, hasService, S3]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: S3 Context triple: [Stuttgart Schwabstraße, hasService, S3]
-
A.
S3
chosen
S3 is a commuter rail line of the Stuttgart S-Bahn network in Germany, connecting the city center with surrounding suburban areas.
-
B.
S3
S3 is a line of the Berlin S-Bahn urban rail network that connects various districts across the Berlin metropolitan area.
-
C.
S3
S3 is one of the commuter rail lines of the Nuremberg S-Bahn network in Germany, serving regional passenger traffic between the city and its surrounding areas.
-
D.
S3
S3 is a line of the Munich S-Bahn suburban rail network that connects central Munich with its surrounding metropolitan area.
-
E.
S3
S3 is a commuter rail line within Germany’s Rhine-Ruhr S-Bahn network, serving regional passenger traffic across the metropolitan area.
- 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_69e0b4af32848190aea80682b44d5d6e |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69b57fa9c819091d12320d46a0cee |
completed | April 20, 2026, 9:32 p.m. |
Created at: April 16, 2026, 11:34 a.m.