Triple
T427799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chemnitz Hauptbahnhof |
E9646
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object |
DB Station&Service
DB Station&Service is a subsidiary of Deutsche Bahn responsible for managing and operating railway stations across Germany.
|
E54321
|
NE FINISHED |
How this triple was built (4 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: DB Station&Service | Statement: [Chemnitz Hauptbahnhof, operator, DB Station&Service]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DB Station&Service Context triple: [Chemnitz Hauptbahnhof, operator, DB Station&Service]
-
A.
NS Stations
NS Stations is a Dutch company responsible for managing and developing railway stations and related facilities across the Netherlands.
-
B.
Reservoir station
Reservoir station is a light rail stop on Boston’s MBTA Green Line that serves the D branch near Cleveland Circle in Brookline.
-
C.
DBE
DBE is the title "Dame Commander of the Order of the British Empire," a high-ranking honor awarded in the British honours system.
-
D.
Akard station
Akard station is a Dallas Area Rapid Transit (DART) light rail station serving the central business district of downtown Dallas, Texas.
-
E.
Shirley station
Shirley station is a commuter rail stop in Shirley, Massachusetts, serving passengers on the MBTA Fitchburg Line.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: DB Station&Service Triple: [Chemnitz Hauptbahnhof, operator, DB Station&Service]
Generated description
DB Station&Service is a subsidiary of Deutsche Bahn responsible for managing and operating railway stations across Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: DB Station&Service Target entity description: DB Station&Service is a subsidiary of Deutsche Bahn responsible for managing and operating railway stations across Germany.
-
A.
NS Stations
NS Stations is a Dutch company responsible for managing and developing railway stations and related facilities across the Netherlands.
-
B.
Reservoir station
Reservoir station is a light rail stop on Boston’s MBTA Green Line that serves the D branch near Cleveland Circle in Brookline.
-
C.
DBE
DBE is the title "Dame Commander of the Order of the British Empire," a high-ranking honor awarded in the British honours system.
-
D.
Akard station
Akard station is a Dallas Area Rapid Transit (DART) light rail station serving the central business district of downtown Dallas, Texas.
-
E.
Shirley station
Shirley station is a commuter rail stop in Shirley, Massachusetts, serving passengers on the MBTA Fitchburg Line.
- F. None of above. chosen
Provenance (5 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_69a2e801e1d48190b505d1dd336b52ac |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eed7f3508190995dcd39586ed614 |
completed | Feb. 28, 2026, 1:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a42f665c2881908850bce36cdf74b8 |
completed | March 1, 2026, 12:21 p.m. |
| NEDg | Description generation | batch_69a43038d2348190a348e6661d27dde4 |
completed | March 1, 2026, 12:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a430f6c6f88190b5aecfe3c4c8957d |
completed | March 1, 2026, 12:28 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.