Triple
T10950828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niendorf |
E258720
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Groß Borstel |
E258719
|
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: Groß Borstel | Statement: [Niendorf, borderedBy, Groß Borstel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Groß Borstel Context triple: [Niendorf, borderedBy, Groß Borstel]
-
A.
Groß Borstel
chosen
Groß Borstel is a residential district of Hamburg, Germany, situated near Hamburg Airport and characterized by a mix of urban housing and green spaces.
-
B.
Himmerich
Himmerich is a hill in Germany’s Siebengebirge range, known for its forested slopes and hiking trails overlooking the Rhine valley.
-
C.
Elmshorn
Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
-
D.
Lübstorf
Lübstorf is a small municipality in northern Germany’s Mecklenburg-Vorpommern state, situated near Lake Schwerin and characterized by its rural setting and natural surroundings.
-
E.
Klein Borstel
Klein Borstel is a small, primarily residential quarter in the north of Hamburg, Germany, known for its quiet streets and proximity to green spaces and the Alster river.
- 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_69d6aa88500c819097d7032ca578e74f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770fc156c8190826e124c13ce7242 |
completed | April 9, 2026, 9:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3e6fd11b08190b587bebf9586f754 |
completed | April 18, 2026, 8:18 p.m. |
Created at: April 8, 2026, 9:23 p.m.