Triple
T919820
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Campbell Soup Company |
E19856
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object |
Milano
Milano is a popular line of chocolate-filled sandwich cookies produced by Pepperidge Farm, a subsidiary of Campbell Soup Company.
|
E187205
|
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: Milano | Statement: [Campbell Soup Company, brand, Milano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Milano Context triple: [Campbell Soup Company, brand, Milano]
-
A.
Milan
Milan is a major Italian metropolis renowned as a global center for fashion, design, finance, and culture.
-
B.
Milan
Milan is a village in northern Ohio best known as the birthplace of inventor Thomas Edison and for its historic canal-era architecture.
-
C.
Turin
Turin is a major city in northern Italy known for its rich history, Baroque architecture, automotive industry, and role as a cultural and economic hub.
-
D.
Bologna
Bologna is a historic city in northern Italy renowned for its medieval architecture, rich culinary tradition, and the University of Bologna, one of the oldest universities in the world.
-
E.
Pavia
Pavia is a historic city in northern Italy, known for its ancient university, medieval architecture, and significant role in Lombardy’s cultural and academic life.
- 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: Milano Triple: [Campbell Soup Company, brand, Milano]
Generated description
Milano is a popular line of chocolate-filled sandwich cookies produced by Pepperidge Farm, a subsidiary of Campbell Soup Company.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Milano Target entity description: Milano is a popular line of chocolate-filled sandwich cookies produced by Pepperidge Farm, a subsidiary of Campbell Soup Company.
-
A.
Milan
Milan is a major Italian metropolis renowned as a global center for fashion, design, finance, and culture.
-
B.
Milan
Milan is a village in northern Ohio best known as the birthplace of inventor Thomas Edison and for its historic canal-era architecture.
-
C.
Turin
Turin is a major city in northern Italy known for its rich history, Baroque architecture, automotive industry, and role as a cultural and economic hub.
-
D.
Bologna
Bologna is a historic city in northern Italy renowned for its medieval architecture, rich culinary tradition, and the University of Bologna, one of the oldest universities in the world.
-
E.
Pavia
Pavia is a historic city in northern Italy, known for its ancient university, medieval architecture, and significant role in Lombardy’s cultural and academic life.
- 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_69a493a099788190a696d9d8408cbaf4 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3103bf48190af3f8d8b2d0fe918 |
completed | March 1, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad67e82a2481909f6d2737f820121a |
completed | March 8, 2026, 12:13 p.m. |
| NEDg | Description generation | batch_69ad68895550819080fdc84d77201e60 |
completed | March 8, 2026, 12:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad6902cb3c8190ba36dc74e1dccb26 |
completed | March 8, 2026, 12:18 p.m. |
Created at: March 1, 2026, 7:40 p.m.