Triple
T5327954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fresh Fruit and Vegetable Program |
E123229
|
entity |
| Predicate | targetSetting |
P1957
|
FINISHED |
| Object | school classrooms |
—
|
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: school classrooms | Statement: [Fresh Fruit and Vegetable Program, targetSetting, school classrooms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetSetting Context triple: [Fresh Fruit and Vegetable Program, targetSetting, school classrooms]
-
A.
target
Indicates that one entity is the intended object, goal, or focus of another entity’s action or attention.
-
B.
setting
chosen
Indicates the place, time, or context in which an event, action, or interaction occurs.
-
C.
targetsUseCase
Indicates that one entity is aimed at or designed to address a particular use case associated with another entity.
-
D.
targetLocation
Indicates the specific place or destination toward which an action, movement, or effect is directed.
-
E.
targetArea
Indicates the specific area or region that is the intended focus or destination of an action or effect.
- 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_69bd46477f9081909d242a327d749466 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd85926c388190a495835caf927624 |
completed | March 20, 2026, 5:36 p.m. |
| PD | Predicate disambiguation | batch_69bd84583dbc819088a03e3afb30178c |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2 p.m.