Triple
T14993973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Medicare for All |
E373907
|
entity |
| Predicate | includesBenefitType |
P75212
|
FINISHED |
| Object | hospital care |
—
|
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: hospital care | Statement: [Medicare for All, includesBenefitType, hospital care]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesBenefitType Context triple: [Medicare for All, includesBenefitType, hospital care]
-
A.
hasBenefitType
chosen
Indicates that an entity is associated with a specific category or type of benefit it provides or receives.
-
B.
benefitAppliesTo
Indicates that a particular benefit is applicable to, or valid for, a specified entity or context.
-
C.
hasBenefit
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
D.
isIndividualBenefit
Indicates that something provides a benefit or advantage to a single individual rather than to a group or collective.
-
E.
benefitsMayInclude
Indicates that one entity lists or specifies possible advantages, gains, or positive outcomes that another entity may receive.
- 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_69d85ccc84388190aa151e5173370c8d |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded716ebb481908224d2d4f7561b03 |
completed | April 15, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69de9a6169b48190a679609febd2d0e3 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:53 a.m.