Triple
T14331949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duisburg port facilities |
E355369
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Port of Duisburg |
E819046
|
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: Port of Duisburg | Statement: [Duisburg port facilities, partOf, Port of Duisburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Port of Duisburg Context triple: [Duisburg port facilities, partOf, Port of Duisburg]
-
A.
Port of Duisburg
chosen
The Port of Duisburg is the world’s largest inland port and a major European logistics hub located in Duisburg, Germany.
-
B.
Port of Cologne
The Port of Cologne is a major inland port on the Rhine River that serves as a key logistics and transportation hub for trade and industry in western Germany.
-
C.
Düsseldorf-Hafen
Düsseldorf-Hafen is a redeveloped harbor district in Düsseldorf known for its modern architecture, media and business centers, and vibrant nightlife along the Rhine.
-
D.
Rheinauhafen
Rheinauhafen is a redeveloped riverside district in Cologne, Germany, known for its modern architecture, cultural attractions, and scenic promenade along the Rhine.
-
E.
Gelsenkirchen port
Gelsenkirchen port is an inland harbor in the German city of Gelsenkirchen that serves as a key industrial and logistics hub in the Ruhr region.
- 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_69d8278fa2108190bc0d0e7939c1eb03 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8c1fb87c81908412c2953243c8e3 |
completed | April 14, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd46943dac819092f5935d9d312949 |
completed | May 8, 2026, 2:12 a.m. |
Created at: April 10, 2026, 1:13 a.m.