Triple
T6475204
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berlin Ostbahnhof |
E146053
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object |
Berlin Ost
Berlin Ost was the former name of Berlin Ostbahnhof, a major railway station in the eastern part of Berlin, Germany.
|
E614856
|
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: Berlin Ost | Statement: [Berlin Ostbahnhof, formerName, Berlin Ost]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Berlin Ost Context triple: [Berlin Ostbahnhof, formerName, Berlin Ost]
-
A.
East Berlin
East Berlin was the Soviet-controlled eastern sector of Berlin that served as the capital of East Germany during the Cold War.
-
B.
Berlin B
Berlin B is one of the public transport fare zones in Berlin, covering the outer areas of the city beyond the central A zone.
-
C.
Berlin
Berlin is the capital and largest city of Germany, historically significant as a focal point of Cold War tensions and a major cultural, political, and economic center in Europe.
-
D.
Berlin
Berlin is a charismatic, calculating, and morally ambiguous mastermind and heist leader in the Spanish television series "Money Heist" (La Casa de Papel).
-
E.
Berlin
Berlin is a major Ethereum network upgrade that introduced various gas cost optimizations and transaction processing improvements to enhance the blockchain’s efficiency and performance.
- 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: Berlin Ost Triple: [Berlin Ostbahnhof, formerName, Berlin Ost]
Generated description
Berlin Ost was the former name of Berlin Ostbahnhof, a major railway station in the eastern part of Berlin, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Berlin Ost Target entity description: Berlin Ost was the former name of Berlin Ostbahnhof, a major railway station in the eastern part of Berlin, Germany.
-
A.
East Berlin
East Berlin was the Soviet-controlled eastern sector of Berlin that served as the capital of East Germany during the Cold War.
-
B.
Berlin B
Berlin B is one of the public transport fare zones in Berlin, covering the outer areas of the city beyond the central A zone.
-
C.
Berlin
Berlin is a charismatic, calculating, and morally ambiguous mastermind and heist leader in the Spanish television series "Money Heist" (La Casa de Papel).
-
D.
Berlin
Berlin is a major Ethereum network upgrade that introduced various gas cost optimizations and transaction processing improvements to enhance the blockchain’s efficiency and performance.
-
E.
Berlin
Berlin is a borough in Camden County, New Jersey, known as a suburban community within the Philadelphia metropolitan area.
- 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_69c008fec7408190af7b146dc63d9750 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06a341360819082f2b5496a1a68b0 |
completed | March 22, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c70adfd6e48190badc31135f9b69a3 |
completed | March 27, 2026, 10:55 p.m. |
| NEDg | Description generation | batch_69c70bb0714c819094e80a2dfc960c99 |
completed | March 27, 2026, 10:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c70c51e0148190be64afb56690b34f |
completed | March 27, 2026, 11:01 p.m. |
Created at: March 22, 2026, 4:50 p.m.