Triple
T12226280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schmalkaldic League |
E291358
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object | City of Duisburg |
E43985
|
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: City of Duisburg | Statement: [Schmalkaldic League, hasMember, City of Duisburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: City of Duisburg Context triple: [Schmalkaldic League, hasMember, City of Duisburg]
-
A.
City of Essen
The City of Essen is a major urban center in Germany’s Ruhr area, historically significant as a medieval ecclesiastical seat and later as an important industrial and coal-mining hub.
-
B.
Duisburg
chosen
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.
-
C.
City of Wesel
The City of Wesel is a historic German town on the Lower Rhine that became an important Reformation and trading center in the early modern period.
-
D.
Recklinghausen
Recklinghausen is a city in the Ruhr area of North Rhine-Westphalia, western Germany, known historically for coal mining and its role as a regional administrative center.
-
E.
Oberhausen
Oberhausen is an industrial city in Germany’s Ruhr region, historically known for its coal and steel production and heavily affected by World War II bombing.
- 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_69d6ab668acc8190963ba424049d6aee |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91ca2101c8190955c36704935036a |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e5c1c988190a80917dfe782a6b6 |
completed | May 2, 2026, 3:55 p.m. |
Created at: April 8, 2026, 9:51 p.m.