Triple
T26689833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Local Control Funding Formula |
E672844
|
entity |
| Predicate | measurementArea |
P163065
|
FINISHED |
| Object | course access |
—
|
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: course access | Statement: [Local Control Funding Formula, measurementArea, course access]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: measurementArea Context triple: [Local Control Funding Formula, measurementArea, course access]
-
A.
performanceMeasureArea
Indicates the specific domain or aspect of performance that a given performance measure is intended to evaluate or quantify.
-
B.
areaApprox
Indicates that one entity’s area is approximately equal to the area of another entity.
-
C.
area
Indicates that one entity has a measured two-dimensional extent or surface size quantified by another entity.
-
D.
roofArea
Indicates the total surface area covered by the roof of a structure.
-
E.
coveredArea
Indicates that one entity occupies or extends over a specific spatial region or surface area associated with another entity.
- 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_69eecda2066c8190a344218afa5e89c1 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f63639a84c81909d700a539b458b42 |
completed | May 2, 2026, 5:36 p.m. |
| PD | Predicate disambiguation | batch_69f631850ae08190a0ba51e4f1e4ccb3 |
completed | May 2, 2026, 5:16 p.m. |
| PDg | Predicate description generation | batch_69f6352df6148190bc10772cd40bd7b3 |
completed | May 2, 2026, 5:32 p.m. |
Created at: April 27, 2026, 3:25 a.m.