Triple
T414393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Social Security system |
E9559
|
entity |
| Predicate | typicalEligibilityAge |
P14567
|
FINISHED |
| Object | 62 |
—
|
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: 62 | Statement: [United States Social Security system, typicalEligibilityAge, 62]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalEligibilityAge Context triple: [United States Social Security system, typicalEligibilityAge, 62]
-
A.
hasLowerAge
Indicates that one entity is younger in age than another entity.
-
B.
ageRange
Indicates the span of ages within which an entity or relationship is considered valid or applicable.
-
C.
eligibilityLevel
Indicates the degree or tier of qualification an entity has for a given benefit, service, or status.
-
D.
eligibilityCriteria
Indicates the conditions or requirements that must be satisfied for an entity to qualify for or be considered eligible for something.
-
E.
militaryAge
Indicates that an entity is within the age range typically considered eligible for military service.
- F. None of above. chosen
Provenance (4 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_69a2e80111fc8190961d5b7c6154123f |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eebde1d881908fb212bfba9d7c67 |
completed | Feb. 28, 2026, 1:33 p.m. |
| PD | Predicate disambiguation | batch_69a2edcff4688190809d83d112ff25a5 |
completed | Feb. 28, 2026, 1:29 p.m. |
| PDg | Predicate description generation | batch_69a2eeb8545c8190a2b8517e7ed5b92e |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:09 p.m.