Triple
T12877797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leipzig metropolitan region |
E308012
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Teutschenthal |
E228674
|
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: Teutschenthal | Statement: [Leipzig metropolitan region, containsCity, Teutschenthal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teutschenthal Context triple: [Leipzig metropolitan region, containsCity, Teutschenthal]
-
A.
Teutschenthal
chosen
Teutschenthal is a municipality in the Saalekreis district of Saxony-Anhalt in central Germany.
-
B.
Treuchtlingen
Treuchtlingen is a small town in the Bavarian region of Germany, known for its location in the Altmühl Valley and its role as a local railway junction and spa destination.
-
C.
Tussenhausen
Tussenhausen is a municipality in the district of Unterallgäu in Bavaria, Germany, known for its rural character and small villages such as Mattsies.
-
D.
Geiersthal
Geiersthal is a small municipality in the Bavarian Forest region of southeastern Germany.
-
E.
Balzhausen
Balzhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
- 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_69f78acb7ee0819093e61c5b8eb7da38 |
completed | May 3, 2026, 5:50 p.m. |
Created at: April 9, 2026, 5:38 p.m.