Triple
T20434273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | S3 (Stuttgart S-Bahn) |
E501210
|
entity |
| Predicate | railwaySign |
P50536
|
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: [S3 (Stuttgart S-Bahn), railwaySign, S3]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: S3 Context triple: [S3 (Stuttgart S-Bahn), railwaySign, S3]
-
A.
S3
S3 is a line of the Berlin S-Bahn urban rail network that connects various districts across the Berlin metropolitan area.
-
B.
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.
-
C.
S3
chosen
S3 is a commuter rail line of the Stuttgart S-Bahn network in Germany, connecting the city center with surrounding suburban areas.
-
D.
S3
S3 is a commuter rail line within Germany’s Rhine-Ruhr S-Bahn network, serving regional passenger traffic across the metropolitan area.
-
E.
S3
S3 is a regional S-Bahn train line in the Rhine-Main area of Germany that connects central Frankfurt with surrounding suburbs and towns.
- 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_69e0b4ab3cfc8190ac9bf32e932316b1 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e685ecf7ac81908e9c2ef4348fb281 |
completed | April 20, 2026, 8 p.m. |
Created at: April 16, 2026, 11:31 a.m.