Triple
T6230921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | S7 |
E139349
|
entity |
| Predicate | viaStation |
P64719
|
FINISHED |
| Object |
Westkreuz
Westkreuz is a major Berlin S-Bahn interchange station that serves as a key junction for multiple suburban rail lines.
|
E578120
|
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: Westkreuz | Statement: [S7, viaStation, Westkreuz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Westkreuz Context triple: [S7, viaStation, Westkreuz]
-
A.
Dresdner Bank
Dresdner Bank was one of Germany’s major commercial banks, historically influential in the country’s financial and industrial development.
-
B.
UniCredit
UniCredit is a major Italian global banking and financial services group headquartered in Milan, with a strong presence across Europe.
-
C.
Deutsche Bank
Deutsche Bank is a major global investment bank and financial services company headquartered in Frankfurt, Germany.
-
D.
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.
-
E.
Danske Bank
Danske Bank is a major Nordic financial institution headquartered in Copenhagen, Denmark, offering a wide range of banking and financial services across Northern Europe.
- 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: Westkreuz Triple: [S7, viaStation, Westkreuz]
Generated description
Westkreuz is a major Berlin S-Bahn interchange station that serves as a key junction for multiple suburban rail lines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Westkreuz Target entity description: Westkreuz is a major Berlin S-Bahn interchange station that serves as a key junction for multiple suburban rail lines.
-
A.
Dresdner Bank
Dresdner Bank was one of Germany’s major commercial banks, historically influential in the country’s financial and industrial development.
-
B.
UniCredit
UniCredit is a major Italian global banking and financial services group headquartered in Milan, with a strong presence across Europe.
-
C.
Deutsche Bank
Deutsche Bank is a major global investment bank and financial services company headquartered in Frankfurt, Germany.
-
D.
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.
-
E.
Danske Bank
Danske Bank is a major Nordic financial institution headquartered in Copenhagen, Denmark, offering a wide range of banking and financial services across Northern Europe.
- 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_69c008afd3148190b71e9eaa60420dd1 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062ec5be4819084d6df2e8dd2a542 |
completed | March 22, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c20de90ba08190be1e0c44b5b13f53 |
completed | March 24, 2026, 4:07 a.m. |
| NEDg | Description generation | batch_69c212d46f648190b72eb440d02178b8 |
completed | March 24, 2026, 4:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c2138541608190a3ea346ff23974a3 |
completed | March 24, 2026, 4:31 a.m. |
Created at: March 22, 2026, 4:22 p.m.