Triple
T160598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Campbell's Soup Cans |
E3275
|
entity |
| Predicate | has32Varieties |
P6222
|
FINISHED |
| Object | different Campbell's soup flavors |
—
|
LITERAL FINISHED |
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: different Campbell's soup flavors | Statement: [Campbell's Soup Cans, has32Varieties, different Campbell's soup flavors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: has32Varieties Context triple: [Campbell's Soup Cans, has32Varieties, different Campbell's soup flavors]
-
A.
parentVariety
Indicates that one variety is the direct parent or source variety from which another variety is derived or developed.
-
B.
numberOfRoseVarieties
Indicates the quantitative count of distinct rose varieties associated with an entity.
-
C.
hasVariant
Indicates that one entity exists as an alternative form, version, or variation of another entity.
-
D.
hasNumberOfCasesApprox
Indicates that an entity is associated with an approximate (not exact) count of cases.
-
E.
viewVariesAmong
Indicates that the way something is viewed, perceived, or interpreted differs across multiple entities or contexts.
- F. None of above. chosen
Provenance (4 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_69a2527757ec819090b8becb2cf1a862 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a25856d934819095460b2ea566eb6b |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a256623704819089d9eeefe05858ce |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a2578329d08190be82e004b8224d2b |
completed | Feb. 28, 2026, 2:48 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.