Triple
T15726531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hardthöhe, Bonn |
E381231
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Bonn-Innenstadt |
E558153
|
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: Bonn-Innenstadt | Statement: [Hardthöhe, Bonn, near, Bonn-Innenstadt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bonn-Innenstadt Context triple: [Hardthöhe, Bonn, near, Bonn-Innenstadt]
-
A.
Bonn-Endenich
Bonn-Endenich is a district of the German city of Bonn, known for its residential character, cultural venues, and proximity to the city center.
-
B.
Bonn-Oberkassel
Bonn-Oberkassel is a district of the German city of Bonn, known for its scenic location along the Rhine and its proximity to the Siebengebirge hills.
-
C.
Cologne-Deutz
Cologne-Deutz is a district on the eastern bank of the Rhine in Cologne, Germany, known for its trade fair grounds, arena, and major transport connections.
-
D.
district Bonn-Zentrum
chosen
District Bonn-Zentrum is the central urban district of Bonn, Germany, encompassing the city’s historic core, key cultural sites, and main commercial areas.
-
E.
Bockenheim
Bockenheim is a lively urban district of Frankfurt am Main known for its mix of residential areas, shops, and university facilities.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04fb357a88190a92641c8a8c20573 |
completed | April 16, 2026, 2:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff82f8b88081909855d3da0346fa25 |
completed | May 9, 2026, 6:54 p.m. |
Created at: April 10, 2026, 4:46 a.m.