Triple
T11745181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bielefeld |
E279260
|
entity |
| Predicate | headquartersOf |
P62
|
FINISHED |
| Object |
Dr. Oetker
Dr. Oetker is a German multinational food company best known for its baking products, desserts, frozen pizzas, and other convenience foods.
|
E944561
|
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: Dr. Oetker | Statement: [Bielefeld, headquartersOf, Dr. Oetker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dr. Oetker Context triple: [Bielefeld, headquartersOf, Dr. Oetker]
-
A.
Knorr
Knorr is a global food brand known for its soups, seasonings, bouillon, and ready-made meal products.
-
B.
Fleischmann
Fleischmann is a German-language surname borne by various notable individuals across fields such as music, science, and the arts.
-
C.
Petit & Fritsen
Petit & Fritsen is a historic Dutch bell foundry renowned for casting church bells and carillons used in notable towers and monuments worldwide.
-
D.
German Mills
German Mills is a residential neighbourhood in the Thornhill area of Ontario, Canada, known for its historic roots and proximity to parks and ravines.
-
E.
Heinz
Heinz is a historic American food processing company best known for its ketchup and other condiments.
- 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: Dr. Oetker Triple: [Bielefeld, headquartersOf, Dr. Oetker]
Generated description
Dr. Oetker is a German multinational food company best known for its baking products, desserts, frozen pizzas, and other convenience foods.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dr. Oetker Target entity description: Dr. Oetker is a German multinational food company best known for its baking products, desserts, frozen pizzas, and other convenience foods.
-
A.
Knorr
Knorr is a global food brand known for its soups, seasonings, bouillon, and ready-made meal products.
-
B.
Fleischmann
Fleischmann is a German-language surname borne by various notable individuals across fields such as music, science, and the arts.
-
C.
Petit & Fritsen
Petit & Fritsen is a historic Dutch bell foundry renowned for casting church bells and carillons used in notable towers and monuments worldwide.
-
D.
German Mills
German Mills is a residential neighbourhood in the Thornhill area of Ontario, Canada, known for its historic roots and proximity to parks and ravines.
-
E.
Heinz
Heinz is a historic American food processing company best known for its ketchup and other condiments.
- 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_69d6ab01038c819080714901502c84fc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4f2a38c8190a682d8dae1ab9415 |
completed | April 10, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f019e4f0988190afe0b92f4c9d8073 |
completed | April 28, 2026, 2:22 a.m. |
| NEDg | Description generation | batch_69f043b3c51c8190a764433e86f1333e |
completed | April 28, 2026, 5:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f05aa351888190a31092e6a9aee26b |
completed | April 28, 2026, 6:58 a.m. |
Created at: April 8, 2026, 9:41 p.m.