Triple
T17991745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bischofsheim an der Rhön |
E430389
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object | Haselbach |
—
|
NE NERFINISHED |
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: Haselbach | Statement: [Bischofsheim an der Rhön, hasSubdivision, Haselbach]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haselbach Context triple: [Bischofsheim an der Rhön, hasSubdivision, Haselbach]
-
A.
Haselbach
chosen
Haselbach is a small municipality in the Straubing-Bogen district of Lower Bavaria in southeastern Germany.
-
B.
Brenkhausen
Brenkhausen is a village and district of the town of Höxter in North Rhine-Westphalia, Germany.
-
C.
Gerlosbach
Gerlosbach is a mountain river in Tyrol, Austria, that flows through the Zillertal Alps before joining the Ziller.
-
D.
Calmbach
Calmbach is a small town in Germany’s Black Forest region, known for its scenic location in the Enz Valley and traditional spa and nature tourism.
-
E.
Hahnbach
Hahnbach is a municipality in the Amberg-Sulzbach district of Bavaria, Germany, known for its rural character and historic Bavarian village charm.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b90364248190a37381adea932f42 |
completed | April 10, 2026, 8:46 a.m. |
| NER | Named-entity recognition | batch_69e4b29fd7648190b7f09ea60c7b96a8 |
completed | April 19, 2026, 10:46 a.m. |
Created at: April 10, 2026, 10:23 a.m.