Triple
T10002650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Public Law 99-272 |
E197363
|
entity |
| Predicate | effectOnIndividuals |
P56117
|
FINISHED |
| Object | provides temporary continuation of employer-sponsored health coverage |
—
|
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: provides temporary continuation of employer-sponsored health coverage | Statement: [Public Law 99-272, effectOnIndividuals, provides temporary continuation of employer-sponsored health coverage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnIndividuals Context triple: [Public Law 99-272, effectOnIndividuals, provides temporary continuation of employer-sponsored health coverage]
-
A.
effectOnOthers
Indicates the impact or influence that one entity’s actions, presence, or state has on other entities.
-
B.
effectOnUser
Indicates how an action, event, or condition influences or impacts a user.
-
C.
effectOnMembers
Indicates the impact or influence that something has on the members of a group or organization.
-
D.
affectedPeople
chosen
Indicates the people who are impacted or influenced by a particular event, action, or condition.
-
E.
effectOnSystem
Indicates the influence, change, or impact that one entity, action, or condition has on the state or behavior of a system.
- 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_69ca82f3b61c81908ecc2c1c96dbc2e4 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdcc9078788190a4e75dd7ff830c63 |
completed | April 2, 2026, 1:55 a.m. |
| PD | Predicate disambiguation | batch_69cd1da2cf9081908a6c0eb5247d0bc2 |
completed | April 1, 2026, 1:29 p.m. |
Created at: March 30, 2026, 8:51 p.m.