Triple
T20826174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mahlberg |
E512704
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Lahr/Schwarzwald |
—
|
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: Lahr/Schwarzwald | Statement: [Mahlberg, locatedNear, Lahr/Schwarzwald]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lahr/Schwarzwald Context triple: [Mahlberg, locatedNear, Lahr/Schwarzwald]
-
A.
Lahr
Lahr is a town in southwestern Germany’s Baden-Württemberg region, situated near the Rhine River opposite Strasbourg and known for its historic center and proximity to the Black Forest.
-
B.
Lautern
Lautern is a historical German locality known as the former residence of Palatine Count John Casimir of Simmern.
-
C.
Wiesloch
Wiesloch is a town in the Rhine-Neckar district of Baden-Württemberg, Germany, known for its historical center and role as a regional commercial hub.
-
D.
Lahr, Germany
chosen
Lahr, Germany is a town in the state of Baden-Württemberg in southwestern Germany, known for its location near the Black Forest and its historical old town.
-
E.
Scharbeutz
Scharbeutz is a Baltic Sea resort town in northern Germany known for its long sandy beaches and seaside tourism.
- 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_69e0b4ce39108190a6e8e5df4f1c8dc5 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2fd8480819099930af691d97477 |
completed | April 21, 2026, 12:21 a.m. |
Created at: April 16, 2026, 12:41 p.m.