Triple
T18072292
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bonn Stadtbahn lines |
E432459
|
entity |
| Predicate | servesMunicipality |
P3936
|
FINISHED |
| Object | Bad Godesberg |
—
|
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 Godesberg | Statement: [Bonn Stadtbahn lines, servesMunicipality, Bad Godesberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Godesberg Context triple: [Bonn Stadtbahn lines, servesMunicipality, Bad Godesberg]
-
A.
Bad Godesberg
chosen
Bad Godesberg is a district in the city of Bonn, Germany, known for its affluent residential areas, former diplomatic missions, and scenic location along the Rhine River.
-
B.
Godesberg
Godesberg is a historic district in Bonn, Germany, known for its medieval castle ruins and role in regional conflicts such as the Cologne War.
-
C.
Bad Driburg
Bad Driburg is a small spa town in North Rhine-Westphalia, Germany, known for its mineral springs and health resorts.
-
D.
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.
-
E.
Berg am Laim
Berg am Laim is a district in the east of Munich, Germany, known for its mix of residential areas, industrial sites, and good transport connections to the city center.
- 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_69d8b9070cac81909fa9473fb1c3f1c7 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ccef022c81909be41b2c3a3ee68e |
completed | April 19, 2026, 12:39 p.m. |
Created at: April 10, 2026, 10:26 a.m.