Triple
T19462814
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gütersloh |
E486915
|
entity |
| Predicate | hasNotableCompany |
P19182
|
FINISHED |
| Object | Arvato |
—
|
NE NERFINISHED |
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: Arvato | Statement: [Gütersloh, hasNotableCompany, Arvato]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arvato Context triple: [Gütersloh, hasNotableCompany, Arvato]
-
A.
Arvato
chosen
Arvato is a global business process outsourcing and services provider specializing in customer relationship management, supply chain management, and digital solutions.
-
B.
Breda
Breda is an Italian industrial company best known for manufacturing railway rolling stock, including trains and trams used in transit systems worldwide.
-
C.
Breda
Breda is a historic city in the southern Netherlands known for its medieval architecture, former status as a military and political center, and vibrant cultural life.
-
D.
Bavier
Bavier is the surname of Frances Bavier, the American actress best known for playing Aunt Bee on the classic television series "The Andy Griffith Show."
-
E.
Westphal
Westphal is a German-origin surname borne by various notable individuals in fields such as sports, politics, and academia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633cd6c148190933b4d6bfe84cbe1 |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 10, 2026, 1:38 p.m.