Triple
T2259623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | False Claims Amendments Act of 1986 |
E50005
|
entity |
| Predicate | sectorImpact |
P8004
|
FINISHED |
| Object | health care industry |
—
|
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: health care industry | Statement: [False Claims Amendments Act of 1986, sectorImpact, health care industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sectorImpact Context triple: [False Claims Amendments Act of 1986, sectorImpact, health care industry]
-
A.
sectorInfluence
chosen
Indicates the degree to which one sector affects, shapes, or exerts control over another sector or over outcomes within that sector.
-
B.
economicImpactRegion
Indicates the region or geographic area that experiences or is affected by a particular economic impact.
-
C.
sectorBenefited
Indicates that a particular sector gains advantage, support, or positive impact from a given action, policy, resource, or entity.
-
D.
socialImpact
Indicates the extent to which an action, entity, or relationship affects society or communities, whether positively or negatively.
-
E.
covid19Impact
Indicates the effect, consequences, or influence that COVID-19 has on a given entity, condition, or situation.
- 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_69a88b01e0048190ba96431b5f990ba9 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc2ea65288190bc8644a07a11dfa9 |
completed | March 7, 2026, 6:17 a.m. |
| PD | Predicate disambiguation | batch_69abbdb592588190ac1ef5e8c54575b1 |
completed | March 7, 2026, 5:55 a.m. |
Created at: March 4, 2026, 7:48 p.m.