Triple
T3928064
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BP |
E93325
|
entity |
| Predicate | hasSubsidiary |
P254
|
FINISHED |
| Object |
Aral AG
Aral AG is a major German brand of fuel stations and petroleum products, widely recognized for its network of service stations across Germany.
|
E399807
|
NE FINISHED |
How this triple was built (4 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: Aral AG | Statement: [BP, hasSubsidiary, Aral AG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aral AG Context triple: [BP, hasSubsidiary, Aral AG]
-
A.
DG AGRI
DG AGRI is the European Commission department responsible for EU policy on agriculture and rural development, including the Common Agricultural Policy.
-
B.
Renk AG
Renk AG is a German engineering company specializing in high-performance transmissions, gear units, and drive technology for military and industrial applications.
-
C.
Deutz AG
Deutz AG is a German manufacturer best known for producing internal combustion engines, particularly for industrial and agricultural applications.
-
D.
Bühler
Bühler is a German-language surname borne by various notable individuals across fields such as politics, sports, and academia.
-
E.
Krauss-Maffei Wegmann
Krauss-Maffei Wegmann is a German defense company specializing in the design and production of armored vehicles and military land systems.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Aral AG Triple: [BP, hasSubsidiary, Aral AG]
Generated description
Aral AG is a major German brand of fuel stations and petroleum products, widely recognized for its network of service stations across Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Aral AG Target entity description: Aral AG is a major German brand of fuel stations and petroleum products, widely recognized for its network of service stations across Germany.
-
A.
DG AGRI
DG AGRI is the European Commission department responsible for EU policy on agriculture and rural development, including the Common Agricultural Policy.
-
B.
Renk AG
Renk AG is a German engineering company specializing in high-performance transmissions, gear units, and drive technology for military and industrial applications.
-
C.
Deutz AG
Deutz AG is a German manufacturer best known for producing internal combustion engines, particularly for industrial and agricultural applications.
-
D.
Bühler
Bühler is a German-language surname borne by various notable individuals across fields such as politics, sports, and academia.
-
E.
Krauss-Maffei Wegmann
Krauss-Maffei Wegmann is a German defense company specializing in the design and production of armored vehicles and military land systems.
- F. None of above. chosen
Provenance (5 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_69aed96bfa1081908f7b30f2c647dee6 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeeda4f9d481908dda1b5a826ab64d |
completed | March 9, 2026, 3:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5287b8d548190a929f14637cb9963 |
completed | March 14, 2026, 9:20 a.m. |
| NEDg | Description generation | batch_69b529ad13d48190995ed79d3c41a69b |
completed | March 14, 2026, 9:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b52a5274ec8190b481a2627e94addb |
completed | March 14, 2026, 9:28 a.m. |
Created at: March 9, 2026, 3:23 p.m.