Triple
T3924966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kölner Haie |
E93252
|
entity |
| Predicate | homeCity |
P263
|
FINISHED |
| Object | Köln |
E35950
|
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: Köln | Statement: [Kölner Haie, homeCity, Köln]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Köln Context triple: [Kölner Haie, homeCity, Köln]
-
A.
Cologne
chosen
Cologne is a historic German city on the Rhine River, renowned for its Gothic cathedral, vibrant cultural scene, and status as a major economic and media hub.
-
B.
Düsseldorf
Düsseldorf is a major German city on the Rhine River known for its fashion and art scenes, modern architecture, and status as an important economic and financial center.
-
C.
Duisburg
Duisburg is a major industrial and port city in western Germany’s Ruhr region, known for its steel production and one of the world’s largest inland harbors.
-
D.
Krefeld
Krefeld is a city in western Germany near the Rhine River, known historically for its textile and silk industry.
-
E.
Koblenz
Koblenz is a historic German city in Rhineland-Palatinate, known for its strategic location at the confluence of the Rhine and Moselle rivers and its well-preserved fortresses and old town.
- 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_69aed96bfa1081908f7b30f2c647dee6 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeed7dacdc8190854ebc13db2d24bc |
completed | March 9, 2026, 3:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bfba1867a481908d3b1c72da4d2535 |
completed | March 22, 2026, 9:44 a.m. |
Created at: March 9, 2026, 3:23 p.m.