Triple
T20961164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sababurg |
E516246
|
entity |
| Predicate | locatedInForest |
P39061
|
FINISHED |
| Object | Reinhardswald forest |
—
|
NE NERFINISHED |
How this triple was built (3 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: Reinhardswald forest | Statement: [Sababurg, locatedInForest, Reinhardswald forest]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Reinhardswald forest Context triple: [Sababurg, locatedInForest, Reinhardswald forest]
-
A.
Reichswald Forest
Reichswald Forest is a large wooded area in western Germany near the Dutch border, historically notable as the site of intense fighting during World War II.
-
B.
Arnsberg Forest
Arnsberg Forest is a large wooded region in North Rhine-Westphalia, Germany, known for its extensive hiking trails, natural landscapes, and protected nature reserves.
-
C.
Deister forest
Deister forest is a wooded hill range in Lower Saxony, Germany, known for its hiking trails, natural landscapes, and proximity to the town of Barsinghausen.
-
D.
Göttingen Forest
Göttingen Forest is a wooded hill range in Lower Saxony, Germany, known for its extensive hiking trails and proximity to the university city of Göttingen.
-
E.
Veldenstein Forest
Veldenstein Forest is a large wooded area in northern Bavaria, Germany, known for its extensive hiking trails, diverse wildlife, and inclusion within the Franconian Switzerland–Veldenstein Forest Nature Park.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Reinhardswald forest Target entity description: Reinhardswald forest is a large, historic woodland in northern Hesse, Germany, famed for its dense beech forests, fairy-tale associations, and medieval sites such as Sababurg Castle.
-
A.
Reichswald Forest
Reichswald Forest is a large wooded area in western Germany near the Dutch border, historically notable as the site of intense fighting during World War II.
-
B.
Arnsberg Forest
Arnsberg Forest is a large wooded region in North Rhine-Westphalia, Germany, known for its extensive hiking trails, natural landscapes, and protected nature reserves.
-
C.
Deister forest
Deister forest is a wooded hill range in Lower Saxony, Germany, known for its hiking trails, natural landscapes, and proximity to the town of Barsinghausen.
-
D.
Göttingen Forest
Göttingen Forest is a wooded hill range in Lower Saxony, Germany, known for its extensive hiking trails and proximity to the university city of Göttingen.
-
E.
Veldenstein Forest
Veldenstein Forest is a large wooded area in northern Bavaria, Germany, known for its extensive hiking trails, diverse wildlife, and inclusion within the Franconian Switzerland–Veldenstein Forest Nature Park.
- F. None of above. chosen
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_69e0b4fde6c48190af1398e7e734629e |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fb6f134081908b1ed48ce708f3d5 |
completed | April 21, 2026, 4:22 a.m. |
Created at: April 16, 2026, 1:31 p.m.