Triple
T18478221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rolandswerth |
E451487
|
entity |
| Predicate | locatedOpposite |
P3232
|
FINISHED |
| Object | Bad Honnef |
—
|
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: Bad Honnef | Statement: [Rolandswerth, locatedOpposite, Bad Honnef]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Honnef Context triple: [Rolandswerth, locatedOpposite, Bad Honnef]
-
A.
Bad Honnef
chosen
Bad Honnef is a spa town on the Rhine in North Rhine-Westphalia, Germany, known for its scenic setting near the Siebengebirge hills and its historical associations with prominent political figures.
-
B.
Bad Berleburg
Bad Berleburg is a spa town in the Siegen-Wittgenstein district of North Rhine-Westphalia, Germany, known for its historic castle and location in the Rothaar Mountains.
-
C.
Bad Oeynhausen
Bad Oeynhausen is a spa town in North Rhine-Westphalia, Germany, renowned for its thermal springs and health resorts.
-
D.
Bad Dürrheim
Bad Dürrheim is a spa town in southwestern Germany known for its health resorts and saline baths.
-
E.
Bad Säckingen
Bad Säckingen is a historic spa town in southwestern Germany on the Rhine River, known for its medieval old town and one of the longest covered wooden bridges in Europe.
- 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_69d8d38465a0819099b9b42d2a662ac1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53064a7548190b712a14ad0c7a477 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 11:35 a.m.