Triple
T14754270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baltic Sea trade routes |
E346688
|
entity |
| Predicate | hasImportantPort |
P6498
|
FINISHED |
| Object | Hamburg |
E7419
|
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: Hamburg | Statement: [Baltic Sea trade routes, hasImportantPort, Hamburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hamburg Context triple: [Baltic Sea trade routes, hasImportantPort, Hamburg]
-
A.
Hamburg
chosen
Hamburg is Germany’s second-largest city and a major northern European port and cultural center on the River Elbe.
-
B.
Bremen
Bremen is a city-state in northwestern Germany comprising the cities of Bremen and Bremerhaven, known for its historic Hanseatic heritage and major port on the Weser River.
-
C.
Bremen
Bremen is a small city in western Georgia, United States, known as a regional hub along major transportation routes and as part of the Atlanta metropolitan area’s outer region.
-
D.
Gotenhafen
Gotenhafen was the German name for the port city of Gdynia in occupied Poland during World War II, used as a major naval base by the Kriegsmarine.
-
E.
Hamburg-Altona
Hamburg-Altona is a major district and transportation hub in western Hamburg, Germany, known for its busy long-distance and regional train station and vibrant urban neighborhoods.
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7d59df08190a86da5048358bd6e |
completed | April 14, 2026, 11:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe729a48288190bf24503af6522677 |
completed | May 8, 2026, 11:32 p.m. |
Created at: April 10, 2026, 1:30 a.m.