Triple
T12635668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Lausick station |
E301756
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object | DB Station&Service |
E54321
|
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: DB Station&Service | Statement: [Bad Lausick station, operator, DB Station&Service]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DB Station&Service Context triple: [Bad Lausick station, operator, DB Station&Service]
-
A.
DB Station&Service
chosen
DB Station&Service is a subsidiary of Deutsche Bahn responsible for managing and operating railway stations across Germany.
-
B.
NS Stations
NS Stations is a Dutch company responsible for managing and developing railway stations and related facilities across the Netherlands.
-
C.
Central station
Central station is a major railway hub providing key train connections for travelers to and from Cambridge city center.
-
D.
Central station
Central station is a key Massachusetts Bay Transportation Authority (MBTA) subway stop on the Red Line located in Cambridge, Massachusetts, serving the Central Square area.
-
E.
Kiest station
Kiest station is a Dallas Area Rapid Transit (DART) light rail stop on the Green Line serving the Kiest Boulevard area in Dallas, Texas.
- 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_69d7bdec9f9c8190b4bac675b7588211 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96147f49c8190b701e1e27e207a95 |
completed | April 10, 2026, 8:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6719886708190823f6f7e94e4d199 |
completed | May 2, 2026, 9:50 p.m. |
Created at: April 9, 2026, 5:16 p.m.