Triple
T17592979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | JTG Daugherty Racing |
E428490
|
entity |
| Predicate | sponsorHistory |
P35612
|
FINISHED |
| Object |
Little Debbie
Little Debbie is a popular American snack cake brand known for its individually wrapped treats such as Swiss Rolls, Oatmeal Creme Pies, and Nutty Buddy bars.
|
E1276127
|
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: Little Debbie | Statement: [JTG Daugherty Racing, sponsorHistory, Little Debbie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Little Debbie Context triple: [JTG Daugherty Racing, sponsorHistory, Little Debbie]
-
A.
Twinkie
Twinkie is a hustling, street-smart high school student in The Fast and the Furious: Tokyo Drift who introduces the protagonist to Tokyo’s underground drift racing scene.
-
B.
Kookie
Kookie is a popular, wisecracking young private detective and cultural icon from the classic American TV series "77 Sunset Strip."
-
C.
Wendy
Wendy is a character portrayed by actress and model Jamie King, known from her work in film and television.
-
D.
Wendy
Wendy is a feminine given name of English origin, popularized by J.M. Barrie’s character Wendy Darling in "Peter Pan."
-
E.
Wendy
Wendy is an animated fantasy film scored by composer Dan Romer.
- 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: Little Debbie Triple: [JTG Daugherty Racing, sponsorHistory, Little Debbie]
Generated description
Little Debbie is a popular American snack cake brand known for its individually wrapped treats such as Swiss Rolls, Oatmeal Creme Pies, and Nutty Buddy bars.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Little Debbie Target entity description: Little Debbie is a popular American snack cake brand known for its individually wrapped treats such as Swiss Rolls, Oatmeal Creme Pies, and Nutty Buddy bars.
-
A.
Twinkie
Twinkie is a hustling, street-smart high school student in The Fast and the Furious: Tokyo Drift who introduces the protagonist to Tokyo’s underground drift racing scene.
-
B.
Kookie
Kookie is a popular, wisecracking young private detective and cultural icon from the classic American TV series "77 Sunset Strip."
-
C.
Wendy
Wendy is a character portrayed by actress and model Jamie King, known from her work in film and television.
-
D.
Wendy
Wendy is a feminine given name of English origin, popularized by J.M. Barrie’s character Wendy Darling in "Peter Pan."
-
E.
Wendy
Wendy is an animated fantasy film scored by composer Dan Romer.
- 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_69d889e1030481909950e140c63255b9 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e469e89acc81908e52138ad4f452c6 |
completed | April 19, 2026, 5:36 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01ddf81ea0819080e9324e3e72cc47 |
completed | May 11, 2026, 1:47 p.m. |
| NEDg | Description generation | batch_6a01dee081c081909a6a6cbce547aba8 |
completed | May 11, 2026, 1:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01df96182c81909bc3f399ccc589f9 |
completed | May 11, 2026, 1:54 p.m. |
Created at: April 10, 2026, 5:51 a.m.