Triple
T16198556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prinzenstraße |
E393134
|
entity |
| Predicate | fareZone |
P844
|
FINISHED |
| Object | Berlin A |
E362110
|
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: Berlin A | Statement: [Prinzenstraße, fareZone, Berlin A]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Berlin A Context triple: [Prinzenstraße, fareZone, Berlin A]
-
A.
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.
-
B.
Berlin AB
chosen
Berlin AB is the central fare zone of Berlin’s public transport network, covering the inner city and surrounding urban areas served by the Verkehrsverbund Berlin-Brandenburg (VBB).
-
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 borough in Camden County, New Jersey, known as a suburban community within the Philadelphia metropolitan area.
- 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_69d87f1e49ac8190a311b54d32990576 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e222dc6b1c8190a3d8a6451ed8b95a |
completed | April 17, 2026, 12:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00078db04081909f7e14b09687ba67 |
completed | May 10, 2026, 4:20 a.m. |
Created at: April 10, 2026, 5:03 a.m.