Triple
T4314295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rhön |
E94149
|
entity |
| Predicate | hasRiverSource |
P947
|
FINISHED |
| Object | Fulda |
E161070
|
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: Fulda | Statement: [Rhön, hasRiverSource, Fulda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fulda Context triple: [Rhön, hasRiverSource, Fulda]
-
A.
Fulda
chosen
Fulda is a historic city in central Germany known for its Baroque architecture and former status as an important monastic and ecclesiastical center.
-
B.
Merseburg
Merseburg is a historic town in the German state of Saxony-Anhalt, known for its medieval cathedral and role as an important cultural and administrative center on the River Saale.
-
C.
Hildesheim
Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
-
D.
Wetzlar
Wetzlar is a historic German city in the state of Hesse, known for its medieval old town and its long tradition in optics and precision engineering.
-
E.
Landsberg
Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
- 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_69b3451886588190a3dd1305ea7c58dc |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b350f4448481908be2c7df9cc71bb9 |
completed | March 12, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5db8e40f0819082a15006a1578405 |
completed | March 14, 2026, 10:05 p.m. |
Created at: March 12, 2026, 11:12 p.m.