Triple
T263696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aid to Dependent Children |
E5807
|
entity |
| Predicate | benefitType |
P2188
|
FINISHED |
| Object | cash assistance |
—
|
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: cash assistance | Statement: [Aid to Dependent Children, benefitType, cash assistance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: benefitType Context triple: [Aid to Dependent Children, benefitType, cash assistance]
-
A.
benefits
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or action.
-
B.
hasBenefit
chosen
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
C.
typeOfIncentive
Indicates the specific kind or category of incentive associated with an entity or action.
-
D.
beneficiaries
Indicates that certain entities receive advantages, profits, or positive outcomes from an action, event, or arrangement.
-
E.
healthcareType
Indicates the category or kind of healthcare service, system, or coverage associated with an entity.
- 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_69a258dd8ea08190ac554a1cc8dfd8c3 |
completed | Feb. 28, 2026, 2:54 a.m. |
| NER | Named-entity recognition | batch_69a25d8e809881908a58c9a4e3ba07c3 |
completed | Feb. 28, 2026, 3:14 a.m. |
| PD | Predicate disambiguation | batch_69a25b6e07748190834022a65ba6d803 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:55 a.m.