Triple
T5316441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rote Insel |
E119161
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Schöneberg |
E13289
|
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: Schöneberg | Statement: [Rote Insel, locatedIn, Schöneberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schöneberg Context triple: [Rote Insel, locatedIn, Schöneberg]
-
A.
Schöneberg
chosen
Schöneberg is a district of Berlin, Germany, historically notable as the site of John F. Kennedy’s famous “Ich bin ein Berliner” speech.
-
B.
Schönewalde
Schönewalde is a town in the state of Brandenburg, Germany, known for hosting a German Air Force base.
-
C.
Petershagen
Petershagen is a small town in North Rhine-Westphalia, Germany, known for its historic architecture and scenic location along the Weser River.
-
D.
Friedrichsdorf
Friedrichsdorf is a town in the German state of Hesse, located north of Frankfurt and known historically for its Huguenot heritage and proximity to the Taunus mountains.
-
E.
Wilmersdorf
Wilmersdorf is a residential district in southwestern Berlin known for its affluent neighborhoods, shopping streets like Kurfürstendamm, and a mix of historic and modern architecture.
- 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_69bd446b57bc8190a513d2e6c40314f3 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd854fd07c8190b4f1c3c8e618c308 |
completed | March 20, 2026, 5:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf110e89548190a5eb0bad6ab0483b |
completed | March 21, 2026, 9:43 p.m. |
Created at: March 20, 2026, 1:54 p.m.