Triple
T13331859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seiffen |
E317592
|
entity |
| Predicate | hasGermanName |
P1435
|
FINISHED |
| Object | Seiffen |
E317592
|
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: Seiffen | Statement: [Seiffen, hasGermanName, Seiffen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seiffen Context triple: [Seiffen, hasGermanName, Seiffen]
-
A.
Seiffen
chosen
Seiffen is a village in Germany’s Ore Mountains renowned for its traditional wooden toy-making and iconic Christmas decorations.
-
B.
Klingenthal
Klingenthal is a small town in the Vogtland region of Saxony, Germany, known for its long tradition of musical instrument making, especially accordions and brass instruments.
-
C.
Meißner
Meißner is a municipality in the Werra-Meißner district of the German state of Hesse, known for its proximity to the Meißner mountain range.
-
D.
Köppern
Köppern is a district of the town of Friedrichsdorf in the Hochtaunus region of Hesse, Germany.
-
E.
Mittweida
Mittweida is a small town in the German state of Saxony, known for its university of applied sciences and historic architecture.
- 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_69d806b4d62c81908d4ced1665414be5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9992fffa0819086610ae3bed2e2f9 |
completed | April 11, 2026, 12:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3237ccf88190974ff1a49c3a874b |
completed | May 8, 2026, 12:45 a.m. |
Created at: April 9, 2026, 9:30 p.m.