Triple
T6358511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ringbahn (Berlin) |
E143051
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Westkreuz |
E578120
|
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: Westkreuz | Statement: [Ringbahn (Berlin), hasStation, Westkreuz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Westkreuz Context triple: [Ringbahn (Berlin), hasStation, Westkreuz]
-
A.
Westkreuz
chosen
Westkreuz is a major Berlin S-Bahn interchange station that serves as a key junction for multiple suburban rail lines.
-
B.
Dresdner Bank
Dresdner Bank was one of Germany’s major commercial banks, historically influential in the country’s financial and industrial development.
-
C.
UniCredit
UniCredit is a major Italian global banking and financial services group headquartered in Milan, with a strong presence across Europe.
-
D.
Deutsche Bank
Deutsche Bank is a major global investment bank and financial services company headquartered in Frankfurt, Germany.
-
E.
Komerční banka
Komerční banka is one of the largest commercial banks in the Czech Republic, offering a wide range of retail, corporate, and investment banking services.
- 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_69c008d7a9c4819098d647ec47776917 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067f72f8481908f9df0c0cdf22a52 |
completed | March 22, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d5f134c8190817037ad933c4d2b |
completed | March 27, 2026, 7:10 a.m. |
Created at: March 22, 2026, 4:32 p.m.