Triple
T3489294
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rhens |
E73685
|
entity |
| Predicate | nearbyTown |
P3883
|
FINISHED |
| Object | Boppard |
E373617
|
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: Boppard | Statement: [Rhens, nearbyTown, Boppard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Boppard Context triple: [Rhens, nearbyTown, Boppard]
-
A.
Boppard
chosen
Boppard is a historic town on the Rhine River in Germany, renowned for its well-preserved medieval architecture, wine culture, and scenic river landscapes.
-
B.
Andernach
Andernach is a historic German town on the Rhine River in Rhineland-Palatinate, known for its medieval architecture and one of the world’s highest cold-water geysers.
-
C.
Bingen am Rhein
Bingen am Rhein is a historic town in Germany’s Rhineland-Palatinate, known as a gateway to the scenic Upper Middle Rhine Valley with its castles, vineyards, and river landscapes.
-
D.
Lemgo
Lemgo is a historic town in the Lippe district of North Rhine-Westphalia, Germany, known for its well-preserved medieval architecture and Hanseatic heritage.
-
E.
Radevormwald
Radevormwald is a small historic town in North Rhine-Westphalia, western Germany, known for its hilly Bergisches Land landscape and traditional textile and metalworking industries.
- 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_69ad85cca8d4819088494e9f3340fab5 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbb92b3ac8190b8675f5a5e9d4408 |
completed | March 8, 2026, 6:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b44ee6f0488190ab4a1c9065c48753 |
completed | March 13, 2026, 5:52 p.m. |
Created at: March 8, 2026, 3:18 p.m.