Triple
T18970244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nabisco |
E464146
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object | Nilla |
—
|
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: Nilla | Statement: [Nabisco, brand, Nilla]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nilla Context triple: [Nabisco, brand, Nilla]
-
A.
Rosaroll
Rosaroll was an Italian Philhellene and military figure known for supporting the Greek War of Independence in the early 19th century.
-
B.
Rolo
Rolo is a diminutive form of the given name Roland, often used as a familiar or affectionate nickname.
-
C.
Sanka
chosen
Sanka is a well-known brand of decaffeinated coffee that became popular in the United States during the 20th century.
-
D.
Nanas
Nanas are a series of colorful, voluptuous female sculptures by Niki de Saint Phalle that celebrate femininity, joy, and empowerment.
-
E.
Helva
Helva is a cyborg "brainship" protagonist in Anne McCaffrey's science fiction stories, known for her witty personality and emotional depth despite being encased in a starship.
- 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_69d8dd008af48190a97ff1c6488edf1b |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d619acbc8190acb49b3fae707758 |
completed | April 20, 2026, 7:30 a.m. |
Created at: April 10, 2026, noon