Triple
T11686797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Calenberg |
E277765
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Pattensen |
E297420
|
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: Pattensen | Statement: [Calenberg, locatedNear, Pattensen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pattensen Context triple: [Calenberg, locatedNear, Pattensen]
-
A.
Pattensen
chosen
Pattensen is a small town in Lower Saxony, Germany, situated just south of Hanover in a predominantly rural and agricultural region.
-
B.
Parsberg
Parsberg is a small Bavarian town in southeastern Germany known for its historic hilltop castle and location along major transport routes between Nuremberg and Regensburg.
-
C.
Knudshoved
Knudshoved is a coastal area on the Danish island of Funen that serves as a key transport hub and former ferry terminal at the western end of the Great Belt crossing.
-
D.
Merrild
Merrild is a coffee brand and company known primarily in Northern Europe, offering a range of ground and whole-bean coffees.
-
E.
Borghorst
Borghorst is a district of the German town Steinfurt in North Rhine-Westphalia, known historically for its textile industry and regional cultural heritage.
- 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_69d6aafe02d881909900d54ad7d4af84 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4654be881909bd0256cf18e25de |
completed | April 10, 2026, 7:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef831d27248190894ffdb12c1ddd4d |
completed | April 27, 2026, 3:39 p.m. |
Created at: April 8, 2026, 9:40 p.m.