Triple
T3873386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elaeagnus |
E92438
|
entity |
| Predicate | nutritionalProperty |
P50062
|
FINISHED |
| Object | high in vitamins |
—
|
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: high in vitamins | Statement: [Elaeagnus, nutritionalProperty, high in vitamins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nutritionalProperty Context triple: [Elaeagnus, nutritionalProperty, high in vitamins]
-
A.
nutritionType
Indicates the specific category or kind of nutritional characteristic or value associated with an entity.
-
B.
milkComposition
Indicates the specific nutrients and components that make up a given sample of milk.
-
C.
proteinContent
Indicates the amount or proportion of protein present in a given entity or substance.
-
D.
isAbundantInFood
chosen
Indicates that a particular nutrient, substance, or component is present in large quantities within a given food.
-
E.
focusesOnNutrition
Indicates that the subject’s primary attention, activity, or content is centered on nutrition-related topics, practices, or goals.
- 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_69aed967448c819086c4b358d37b25aa |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1515c688190a38332aedeed8a76 |
completed | March 9, 2026, 4:12 p.m. |
| PD | Predicate disambiguation | batch_69aee7574c408190893e70bf80514838 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:20 p.m.