Triple
T11611749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Supplementary Feeding Program |
E275399
|
entity |
| Predicate | beneficiaryAgeGroup |
P2736
|
FINISHED |
| Object | preschool children |
—
|
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: preschool children | Statement: [Supplementary Feeding Program, beneficiaryAgeGroup, preschool children]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: beneficiaryAgeGroup Context triple: [Supplementary Feeding Program, beneficiaryAgeGroup, preschool children]
-
A.
beneficiaryType
Indicates the type or category of beneficiary that receives or is intended to receive the benefit or outcome of an action or resource.
-
B.
typicalEligibilityAge
Indicates the usual or standard age at which an entity qualifies for or becomes eligible for a particular status, benefit, or activity.
-
C.
ageRange
chosen
Indicates the span of ages within which an entity or relationship is considered valid or applicable.
-
D.
estimatedNumberOfBeneficiaries
Indicates the approximate count of individuals or entities expected to receive benefits from something.
-
E.
eligibleBeneficiaries
Indicates that certain parties meet the required conditions to receive benefits or entitlements under a given rule or program.
- 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_69d6aaf84b548190ac072e4fb89ae18f |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a043a3c08190a20cbc2ba5a8d218 |
completed | April 10, 2026, 7:01 a.m. |
| PD | Predicate disambiguation | batch_69d85dd6503c819081f9045e9d5c4f3f |
completed | April 10, 2026, 2:17 a.m. |
Created at: April 8, 2026, 9:38 p.m.