Triple
T3823814
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Coffee |
E88636
|
entity |
| Predicate | hasCompetitor |
P1375
|
FINISHED |
| Object |
Keurig
Keurig is a beverage brewing company best known for its single-serve coffee pod machines widely used in homes and offices.
|
E148612
|
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: Keurig | Statement: [Mr. Coffee, hasCompetitor, Keurig]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keurig Context triple: [Mr. Coffee, hasCompetitor, Keurig]
-
A.
Mr. Coffee
Mr. Coffee is a popular American brand best known for its automatic drip coffee makers and related coffee appliances for home use.
-
B.
Nespresso
Nespresso is a premium coffee brand known for its single-serve espresso machines and coffee capsules marketed worldwide.
-
C.
Faema
Faema was a prominent professional Italian cycling team of the 1950s and 1960s, best known for sponsoring and supporting legendary riders such as Eddy Merckx.
-
D.
Hamilton Beach
Hamilton Beach is a small residential neighborhood in the Howard Beach area of Queens, New York City, known for its waterfront location and proximity to Jamaica Bay.
-
E.
Britta
Britta is a feminine given name of Germanic origin commonly used in German-speaking and Scandinavian countries.
- 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: Keurig Triple: [Mr. Coffee, hasCompetitor, Keurig]
Generated description
Keurig is a beverage brewing company best known for its single-serve coffee pod machines widely used in homes and offices.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Keurig Target entity description: Keurig is a beverage brewing company best known for its single-serve coffee pod machines widely used in homes and offices.
-
A.
Mr. Coffee
Mr. Coffee is a popular American brand best known for its automatic drip coffee makers and related coffee appliances for home use.
-
B.
Nespresso
chosen
Nespresso is a premium coffee brand known for its single-serve espresso machines and coffee capsules marketed worldwide.
-
C.
Faema
Faema was a prominent professional Italian cycling team of the 1950s and 1960s, best known for sponsoring and supporting legendary riders such as Eddy Merckx.
-
D.
Hamilton Beach
Hamilton Beach is a small residential neighborhood in the Howard Beach area of Queens, New York City, known for its waterfront location and proximity to Jamaica Bay.
-
E.
Britta
Britta is a feminine given name of Germanic origin commonly used in German-speaking and Scandinavian countries.
- F. None of above.
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_69aed9538cf881909d9ce8ca4ac7c18c |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeeb6223808190aee0c15259eeeecd |
completed | March 9, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4fb4c70008190bb8712f46f40d6f8 |
completed | March 14, 2026, 6:08 a.m. |
| NEDg | Description generation | batch_69b4fc5747208190a7af99c10014080e |
completed | March 14, 2026, 6:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4fcc395ec8190a781e982b935c2f6 |
completed | March 14, 2026, 6:14 a.m. |
Created at: March 9, 2026, 3:17 p.m.