Triple
T14609789
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kurhessen |
E342926
|
entity |
| Predicate | hasSubregion |
P285
|
FINISHED |
| Object | Ziegenhain |
E453622
|
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: Ziegenhain | Statement: [Kurhessen, hasSubregion, Ziegenhain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ziegenhain Context triple: [Kurhessen, hasSubregion, Ziegenhain]
-
A.
Ziegenhain
chosen
Ziegenhain is a historic town in the German state of Hesse, known for its medieval fortifications and role in regional conflicts.
-
B.
Aulhausen
Aulhausen is a district of the town Rüdesheim am Rhein in the Rheingau region of Hesse, Germany, known for its scenic vineyards and rural character.
-
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.
Boltenhagen
Boltenhagen is a Baltic Sea seaside resort town in northern Germany known for its beaches and tourism.
-
E.
Trostberg
Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb44f0dd48190a78662b5998a6722 |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff1a5c72008190a3c4df20480850c9 |
completed | May 9, 2026, 11:28 a.m. |
Created at: April 10, 2026, 1:25 a.m.