Triple
T10209901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arvato |
E242298
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object | Gütersloh, Germany |
E486915
|
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: Gütersloh, Germany | Statement: [Arvato, headquartersLocation, Gütersloh, Germany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gütersloh, Germany Context triple: [Arvato, headquartersLocation, Gütersloh, Germany]
-
A.
Giessen, Germany
Giessen, Germany is a central German university town in the state of Hesse, known for its large student population and academic institutions.
-
B.
Schröttinghausen, Germany
Schröttinghausen is a small locality in Germany best known as the birthplace of influential astronomer Walter Baade.
-
C.
Gütersloh
chosen
Gütersloh is a city in the German state of North Rhine-Westphalia known for being the headquarters of major companies like Bertelsmann and Miele.
-
D.
Minden, Germany
Minden, Germany is a historic town in North Rhine-Westphalia known for its strategic location on the Weser River and its role in significant military events such as the Battle of Minden.
-
E.
Krefeld, Germany
Krefeld, Germany is an industrial city in North Rhine-Westphalia known historically for its textile and silk production.
- 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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d395fbed008190b66996f5bb397853 |
completed | April 6, 2026, 11:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6a7f6730081908b941eaeb6c00993 |
completed | April 8, 2026, 7:09 p.m. |
Created at: April 6, 2026, 11 a.m.