Triple
T13698346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rübeland |
E328446
|
entity |
| Predicate | hasNearbyTown |
P3883
|
FINISHED |
| Object | Elbingerode |
E872687
|
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: Elbingerode | Statement: [Rübeland, hasNearbyTown, Elbingerode]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elbingerode Context triple: [Rübeland, hasNearbyTown, Elbingerode]
-
A.
Elbingerode
chosen
Elbingerode is a small town in central Germany’s Harz region, known for its surrounding forests, karst landscapes, and outdoor recreation opportunities.
-
B.
Großalmerode
Großalmerode is a small town in the German state of Hesse, known historically for its clay and porcelain industry and its location in the hilly, forested region of northern Hesse.
-
C.
Korbach
Korbach is a historic town in the German state of Hesse, known as the district seat of Waldeck-Frankenberg and for its well-preserved medieval old town.
-
D.
Eckental
Eckental is a market town and municipality in the Erlangen-Höchstadt district of Bavaria, Germany.
-
E.
Elsterwerda
Elsterwerda is a small town in the state of Brandenburg in eastern Germany, known for its regional railway connections and location near the Elbe-Elster district.
- 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_69d8076ff62081908a7bd79889edd7a0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc878b57c819094e7ea6d1a64211f |
completed | April 12, 2026, 4:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5b543308190a86e715106641484 |
completed | May 8, 2026, 12:23 p.m. |
Created at: April 9, 2026, 9:54 p.m.