Triple
T9282217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aachen district (Städteregion Aachen) |
E223095
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Eschweiler |
E375285
|
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: Eschweiler | Statement: [Aachen district (Städteregion Aachen), contains, Eschweiler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eschweiler Context triple: [Aachen district (Städteregion Aachen), contains, Eschweiler]
-
A.
Eschweiler
chosen
Eschweiler is a town in western Germany near Aachen, known for its industrial history and location in the state of North Rhine-Westphalia.
-
B.
Neunkirchen
Neunkirchen is a town in southwestern Germany known as one of the major urban centers and former industrial hubs of the state of Saarland.
-
C.
Neuß
Neuß is an alternative spelling of Neuss, a historic city on the Rhine in North Rhine-Westphalia, Germany.
-
D.
Neuss
Neuss is a city in western Germany, near Düsseldorf, known as an administrative and commercial center with historical roots dating back to Roman times.
-
E.
Wermelskirchen
Wermelskirchen is a small town in North Rhine-Westphalia, Germany, known for its location in the hilly Bergisches Land region and its traditional half-timbered architecture.
- 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_69ca842123588190b3f2e1a69037d141 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd081c2b048190aa6930de3bf2f87f |
completed | April 1, 2026, 11:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12cc59610819090adf2b4c3f5cec3 |
completed | April 4, 2026, 3:22 p.m. |
Created at: March 30, 2026, 7:34 p.m.