Triple
T19312947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ilm |
E483016
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Mellingen |
—
|
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: Mellingen | Statement: [Ilm, flowsThrough, Mellingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mellingen Context triple: [Ilm, flowsThrough, Mellingen]
-
A.
Mellingen
Mellingen is a small Swiss town in the canton of Aargau known for its historic old town and riverside setting along the Reuss.
-
B.
Mellingen
chosen
Mellingen is a small municipality in the German state of Thuringia, known for its location near the historic city of Weimar.
-
C.
Millingen
Millingen is a village and district within the town of Rheinberg in North Rhine-Westphalia, Germany.
-
D.
Magglingen
Magglingen is a Swiss village in the canton of Bern known as a national center for sports and physical education.
-
E.
Menzingen
Menzingen is a municipality in the canton of Zug in central Switzerland, known for its rural landscape and location in the pre-Alpine region.
- 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_69d8e8d04d5c8190baa816986f2b1d1e |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e604cf25c081908a30814b15d78c25 |
completed | April 20, 2026, 10:49 a.m. |
Created at: April 10, 2026, 1:32 p.m.