Triple
T3683776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Public Law 88-38 |
E78173
|
entity |
| Predicate | effectiveArea |
P50295
|
FINISHED |
| Object | private sector employers covered by FLSA |
—
|
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: private sector employers covered by FLSA | Statement: [Public Law 88-38, effectiveArea, private sector employers covered by FLSA]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectiveArea Context triple: [Public Law 88-38, effectiveArea, private sector employers covered by FLSA]
-
A.
representedArea
Indicates that one entity serves as a representation or depiction of a particular area or region.
-
B.
areaCControl
Indicates that an entity exercises control or authority over a specific geographic area or region.
-
C.
affectedArea
Indicates the specific region or extent over which an event, condition, or influence has an impact.
-
D.
areaSquareDegrees
Indicates the extent of a region or object measured as an area on the sky in square degrees.
-
E.
targetArea
Indicates the specific area or region that is the intended focus or destination of an action or effect.
- 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_69ad85e18c1c8190be8aafb227f39f48 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc49644d08190866d6c5df9d2b48d |
completed | March 8, 2026, 6:48 p.m. |
| PD | Predicate disambiguation | batch_69adb84be1fc81909721c871babb4633 |
completed | March 8, 2026, 5:56 p.m. |
| PDg | Predicate description generation | batch_69adb97cdb788190a5ce96b21bd157ab |
completed | March 8, 2026, 6:01 p.m. |
Created at: March 8, 2026, 3:26 p.m.