Triple
T12877847
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leipzig metropolitan region |
E308012
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Glauchau |
E850606
|
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: Glauchau | Statement: [Leipzig metropolitan region, containsCity, Glauchau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Glauchau Context triple: [Leipzig metropolitan region, containsCity, Glauchau]
-
A.
Glauchau
chosen
Glauchau is a town in the Free State of Saxony in eastern Germany, known for its historic castles and location in the industrial region of Zwickau.
-
B.
Crimmitschau
Crimmitschau is a town in the German state of Saxony, historically known for its textile industry and located within the broader Leipzig metropolitan area.
-
C.
Premnitz
Premnitz is a small town in the Havelland region of Brandenburg, Germany, situated on the Havel River and known historically for its chemical industry.
-
D.
Riesa
Riesa is a town in the German state of Saxony, situated on the Elbe River and known historically as an important regional railway and industrial center.
-
E.
Bischofswerda
Bischofswerda is a small town in the Saxony region of eastern Germany, known as a local commercial and transport hub near the city of Dresden.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d970fa8474819086a8af3c90f3ca84 |
completed | April 10, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe967a96708190b8688e84fdb270b0 |
completed | May 9, 2026, 2:05 a.m. |
Created at: April 9, 2026, 5:38 p.m.