Triple
T5936083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langen Foundation building |
E132048
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Neuss |
E147996
|
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: Neuss | Statement: [Langen Foundation building, location, Neuss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Neuss Context triple: [Langen Foundation building, location, Neuss]
-
A.
Neuss
chosen
Neuss is a city in western Germany, near Düsseldorf, known as an administrative and commercial center with historical roots dating back to Roman times.
-
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.
Eschweiler
Eschweiler is a town in western Germany near Aachen, known for its industrial history and location in the state of North Rhine-Westphalia.
-
D.
Diekirch
Diekirch is a town in northern Luxembourg known for its role in World War II, particularly during the country's liberation, and for its national military museum.
-
E.
Andernach
Andernach is a historic German town on the Rhine River in Rhineland-Palatinate, known for its medieval architecture and one of the world’s highest cold-water geysers.
- 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_69c0085c55dc8190aa90e242c956e2fa |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c038eb56ec81909be03509730b7cc1 |
completed | March 22, 2026, 6:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16e882cdc819082b46b9380c430ad |
completed | March 23, 2026, 4:47 p.m. |
Created at: March 22, 2026, 4:01 p.m.