Triple
T22728898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hoeven |
E562072
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Halderberge |
—
|
NE NERFINISHED |
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: Halderberge | Statement: [Hoeven, partOf, Halderberge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Halderberge Context triple: [Hoeven, partOf, Halderberge]
-
A.
Halderberge
chosen
Halderberge is a municipality in the Dutch province of North Brabant, known for its historic towns such as Oudenbosch and its mix of rural landscapes and small urban centers.
-
B.
Hasliberg
Hasliberg is a Swiss alpine village and municipality in the canton of Bern, known for its mountain scenery and ski and hiking resort facilities.
-
C.
Hornberg
Hornberg is a small town in the Black Forest region of Baden-Württemberg, Germany, known for its scenic landscape and traditional cuckoo clock craftsmanship.
-
D.
Hangelsberg
Hangelsberg is a village in the German state of Brandenburg, known as a district of the municipality Grünheide (Mark) in the Oder-Spree region.
-
E.
Wackersberg
Wackersberg is a rural Bavarian municipality in southern Germany, known for its scenic Alpine foothills and traditional village character.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e24550859c81908727d91efc3a81b4 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1792be7d88190b6d7d79041fcba25 |
completed | April 29, 2026, 3:21 a.m. |
Created at: April 17, 2026, 3:21 p.m.