Triple
T15889598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | S Babelsberg station |
E385280
|
entity |
| Predicate | servedBy |
P82
|
FINISHED |
| Object | S-Bahn |
E119085
|
NE FINISHED |
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: S-Bahn | Statement: [S Babelsberg station, servedBy, S-Bahn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: S-Bahn Context triple: [S Babelsberg station, servedBy, S-Bahn]
-
A.
S-Bahn
chosen
The S-Bahn is a German urban and suburban rapid transit rail system that connects city centers with surrounding metropolitan regions.
-
B.
S-Bahn Ringbahn
The S-Bahn Ringbahn is Berlin’s circular urban rail line that loops around the inner city, connecting numerous districts and major transport hubs.
-
C.
Berlin S-Bahn
The Berlin S-Bahn is a rapid transit railway network serving Berlin and its surrounding areas, integrating suburban and urban rail services across the metropolitan region.
-
D.
Rhine-Ruhr S-Bahn
The Rhine-Ruhr S-Bahn is a regional rapid transit network serving the densely populated Rhine-Ruhr metropolitan area in western Germany, connecting major cities such as Duisburg, Düsseldorf, Essen, and Dortmund.
-
E.
Hamburg S-Bahn
The Hamburg S-Bahn is a rapid transit and commuter rail network serving the city of Hamburg and its surrounding metropolitan region in northern Germany.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d86da5b800819083a31be937d738b0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1561d5c28819094c3541d917a4433 |
completed | April 16, 2026, 9:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb59f979c8190826e35a16e295704 |
completed | May 9, 2026, 10:30 p.m. |
Created at: April 10, 2026, 4:51 a.m.