Triple
T12580125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frankfurt Taunusanlage station |
E300312
|
entity |
| Predicate | servedByLine |
P1293
|
FINISHED |
| Object | S5 |
E987073
|
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: S5 | Statement: [Frankfurt Taunusanlage station, servedByLine, S5]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: S5 Context triple: [Frankfurt Taunusanlage station, servedByLine, S5]
-
A.
S5
S5 is a line of the Berlin S-Bahn rapid transit network serving routes between central Berlin and its eastern suburbs.
-
B.
S5
S5 is a commuter rail line of the Stuttgart S-Bahn network serving the Stuttgart metropolitan area in Germany.
-
C.
S5
S5 is the symmetric group on five elements, a fundamental non-abelian finite group that plays a key role in permutation group theory and Galois theory.
-
D.
S5
S5 is a regional S-Bahn rail line within Germany’s Rhine-Ruhr metropolitan transit network, connecting key cities and suburbs in the area.
-
E.
S5
chosen
S5 is one of the commuter rail lines of the Rhine-Main S-Bahn network serving the Frankfurt metropolitan area in 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_69d7bde87b648190bcd0266e9efde098 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954b867dc8190af8a70f797e4d133 |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6559ba5108190b85be540a405eec8 |
completed | May 2, 2026, 7:50 p.m. |
Created at: April 9, 2026, 5:01 p.m.