Triple
T19892802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pirouette |
E478073
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Pepperidge Farm |
—
|
NE NERFINISHED |
How this triple was built (2 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: Pepperidge Farm | Statement: [Pirouette, producer, Pepperidge Farm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pepperidge Farm Context triple: [Pirouette, producer, Pepperidge Farm]
-
A.
Pepperidge Farm
chosen
Pepperidge Farm is an American food company best known for its cookies, crackers, and baked goods such as Milano cookies and Goldfish crackers.
-
B.
Nabisco
Nabisco is a major American snack food company best known for iconic brands like Oreo cookies and Ritz crackers.
-
C.
Triscuit
Triscuit is a popular American brand of baked whole-grain wheat crackers known for their woven texture and simple ingredients.
-
D.
Hillshire Farm
Hillshire Farm is a popular American brand known for its packaged meats, including sausages, deli meats, and smoked meats.
-
E.
Pepperidge Farm Milano Lemon
Pepperidge Farm Milano Lemon is a variety of Milano cookies featuring a crisp, buttery cookie with a sweet-tart lemon-flavored filling.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51f32b08190b3687f4f60353250 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6590f9eb481909104a6a404c5c331 |
completed | April 20, 2026, 4:49 p.m. |
Created at: April 10, 2026, 1:52 p.m.