Triple
T20023108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | D-Zug |
E494910
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | D-Zug |
—
|
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: D-Zug | Statement: [D-Zug, shortName, D-Zug]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: D-Zug Context triple: [D-Zug, shortName, D-Zug]
-
A.
D-Zug
chosen
D-Zug was a former class of fast long-distance passenger trains in German-speaking countries, known for providing relatively quick intercity connections before being largely superseded by newer service categories.
-
B.
Voralpen-Express
The Voralpen-Express is a scenic Swiss intercity train service that connects eastern and central Switzerland across the pre-Alpine region.
-
C.
Interregio-Express
Interregio-Express is a category of German regional express trains that provide faster, limited-stop connections between cities and regions.
-
D.
Anhalter Bahn
Anhalter Bahn is a historic German railway line that once connected Berlin with central and southern Germany, playing a major role in long-distance passenger traffic before World War II.
-
E.
Allegro high-speed train
The Allegro high-speed train is a tilting passenger service that operated between Helsinki, Finland, and St. Petersburg, Russia, significantly reducing travel time on this international route.
- 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6628a1ecc8190bf6ee0bedb61e0b8 |
completed | April 20, 2026, 5:29 p.m. |
Created at: April 11, 2026, 3:35 p.m.