Triple
T22183333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wien Mitte railway station |
E548227
|
entity |
| Predicate | railwayLineServed |
P848
|
FINISHED |
| Object | S3 line |
—
|
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 line | Statement: [Wien Mitte railway station, railwayLineServed, S3 line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: S3 line Context triple: [Wien Mitte railway station, railwayLineServed, S3 line]
-
A.
S3 line
The S3 line is a route of the Rhine-Main S-Bahn network serving the Frankfurt metropolitan area in Germany.
-
B.
S3 line
The S3 line is a route of the Berlin S-Bahn urban rail network that connects various districts across the city and serves stations such as Berlin Grunewald.
-
C.
S3 line
chosen
The S3 line is a suburban railway service of the Vienna S-Bahn network that connects central Vienna with surrounding regional areas, including Praterstern station.
-
D.
S3 line
The S3 line is a commuter rail service within the Zürich S-Bahn network that connects Zürich with its surrounding suburbs and regional destinations.
-
E.
S3 Line
The S3 Line is a rapid transit route within the Nanjing Metro system in Nanjing, China.
- 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_69e11e3e0c7c8190b30d278845e2497e |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12aa687808190b9959d4e91db948a |
completed | April 28, 2026, 9:46 p.m. |
Created at: April 16, 2026, 8:35 p.m.