Triple
T2025158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fürth |
E44190
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Gera |
E22181
|
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: Gera | Statement: [Fürth, twinTown, Gera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gera Context triple: [Fürth, twinTown, Gera]
-
A.
Gera
chosen
Gera is a city in the German state of Thuringia, known for its industrial heritage and historic architecture along the White Elster river.
-
B.
Morava
Morava is a Central European river that forms part of the border between Austria, the Czech Republic, and Slovakia before joining the Danube near Bratislava.
-
C.
Lahn
The Lahn is a river in western Germany that flows through the states of North Rhine-Westphalia, Hesse, and Rhineland-Palatinate before joining the Rhine.
-
D.
Werre
The Werre is a river in North Rhine-Westphalia, Germany, that flows through towns such as Detmold and Herford before joining the Weser.
-
E.
Schwabach
Schwabach is a historic town in northern Bavaria, Germany, known for its traditional gold-beating craft and well-preserved old town.
- 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_69a8891201bc8190aca837be6de41579 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb8f3faa08190a48ae1355d6e009f |
completed | March 7, 2026, 5:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae1fead86c8190b3b247e88aad30f9 |
completed | March 9, 2026, 1:18 a.m. |
Created at: March 4, 2026, 7:38 p.m.