Triple
T25074549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snezhanka salad |
E628010
|
entity |
| Predicate | containsNut |
P56691
|
FINISHED |
| Object | walnut |
—
|
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: walnut | Statement: [Snezhanka salad, containsNut, walnut]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsNut Context triple: [Snezhanka salad, containsNut, walnut]
-
A.
hasNutrientContent
Indicates that one entity contains or provides a specified amount or type of nutrient relative to another entity or standard.
-
B.
usesNutType
chosen
Indicates that one entity employs or incorporates a specific type of nut in its structure, function, or composition.
-
C.
hasNutrientStatus
Indicates that an entity possesses a particular nutritional condition or level, such as being deficient, sufficient, or excessive in specific nutrients.
-
D.
containsNoodles
Indicates that one entity (typically a dish or food item) includes noodles as one of its components.
-
E.
hasProtein
Indicates that one entity contains, includes, or is associated with a specific protein.
- F. None of above.
Provenance (3 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_69e2ff2d71dc8190b4758e57d643cbe4 |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f676c440708190a4b9974e95d2291a |
completed | May 2, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69f675fd59608190b246383435e68fce |
completed | May 2, 2026, 10:09 p.m. |
Created at: April 18, 2026, 6:20 a.m.