Triple
T17493512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gersprenz |
E425988
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Groß-Umstadt |
—
|
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: Groß-Umstadt | Statement: [Gersprenz, flowsThrough, Groß-Umstadt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Groß-Umstadt Context triple: [Gersprenz, flowsThrough, Groß-Umstadt]
-
A.
Groß-Umstadt
chosen
Groß-Umstadt is a historic small town in southern Hesse, Germany, known for its wine-growing tradition and medieval old town.
-
B.
Großeibstadt
Großeibstadt is a small municipality in the Rhön-Grabfeld district of northern Bavaria, Germany, known for its rural character and Franconian village setting.
-
C.
Schwalmstadt
Schwalmstadt is a small town in the Schwalm-Eder district of northern Hesse, Germany, known for its historic half-timbered architecture and picturesque setting in the Schwalm River valley.
-
D.
Michelstadt
Michelstadt is a historic town in the Odenwald region of southern Hesse, Germany, known for its well-preserved medieval timber-framed buildings and picturesque old town.
-
E.
Höchheim
Höchheim is a small municipality in the Rhön-Grabfeld district of northern Bavaria, Germany.
- 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_69d889dccf7481909264a1844a2e9100 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e451d6bd548190b4c6fae27c2a9ae8 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 5:48 a.m.