Triple
T11231832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlottenburg-Wilmersdorf |
E265840
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Halensee |
E588599
|
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: Halensee | Statement: [Charlottenburg-Wilmersdorf, contains, Halensee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Halensee Context triple: [Charlottenburg-Wilmersdorf, contains, Halensee]
-
A.
Halensee
chosen
Halensee is a railway station in Berlin that serves the city's circular Ringbahn line, connecting the Halensee district to the wider urban rail network.
-
B.
Heiligensee
Heiligensee is a residential and partly lakeside locality in the northwest of Berlin, known for its green spaces and village-like character within the borough of Reinickendorf.
-
C.
Müggelsee
Müggelsee is the largest lake in Berlin, Germany, known for its popular recreational areas and natural surroundings.
-
D.
Egelsee
Egelsee is a locality within the Austrian city of Krems an der Donau, known for its residential character and proximity to the Wachau cultural landscape.
-
E.
Fleesensee
Fleesensee is a large lake in northeastern Germany known for its popular holiday resorts, water sports, and scenic natural surroundings.
- 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_69d6aac656d48190b275efaa7d6074ee |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9026e1c81909456ac946bbba972 |
completed | April 9, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4ad49b5cc8190b99cb2cd8de72109 |
completed | April 19, 2026, 10:24 a.m. |
Created at: April 8, 2026, 9:30 p.m.