Triple
T20029454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bergstraße district |
E495081
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Lorsch |
—
|
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: Lorsch | Statement: [Bergstraße district, contains, Lorsch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lorsch Context triple: [Bergstraße district, contains, Lorsch]
-
A.
Lorsch
chosen
Lorsch is a small town in the German state of Hesse best known for its UNESCO World Heritage–listed former imperial Abbey of Lorsch.
-
B.
Speyer
Speyer is a historic city in southwestern Germany on the Rhine River, renowned for its Romanesque imperial cathedral, a UNESCO World Heritage Site.
-
C.
Abbey of Lorsch
The Abbey of Lorsch is a former Imperial Benedictine monastery in present-day Germany, renowned for its Carolingian architecture and its status as a UNESCO World Heritage Site.
-
D.
Willanzheim
Willanzheim is a small municipality in the Kitzingen district of Bavaria, Germany, known for its rural character and Franconian wine-growing tradition.
-
E.
Meersburg
Meersburg is a historic town in southern Germany known for its medieval castle, picturesque old town, and scenic location on the shores of Lake Constance.
- 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e662908df081909a6c8ccf0dd90fff |
completed | April 20, 2026, 5:29 p.m. |
Created at: April 11, 2026, 3:36 p.m.