Triple
T19680583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AGR |
E472573
|
entity |
| Predicate | benefitsSimilarTo |
P136895
|
FINISHED |
| Object | Regular active duty service |
—
|
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: Regular active duty service | Statement: [AGR, benefitsSimilarTo, Regular active duty service]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: benefitsSimilarTo Context triple: [AGR, benefitsSimilarTo, Regular active duty service]
-
A.
benefitsAre
Indicates that certain advantages, gains, or positive outcomes are possessed by or accrue to a particular entity or group.
-
B.
hasDifferentBenefitsThan
Indicates that the benefits provided by one entity are not the same as those provided by another entity.
-
C.
benefits
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or action.
-
D.
benefitAppliesTo
Indicates that a particular benefit is applicable to, or valid for, a specified entity or context.
-
E.
relatedBenefit
Indicates that one entity provides an advantage, gain, or positive outcome that is connected or attributable to another entity.
- 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_69d8e514f2e08190ba70a4449519d218 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e641bf97348190bc31b00ed4ec6cad |
completed | April 20, 2026, 3:09 p.m. |
| PD | Predicate disambiguation | batch_69e53039ea808190a9106a53f564ab92 |
completed | April 19, 2026, 7:42 p.m. |
| PDg | Predicate description generation | batch_69e532bbedf081908d801600e2af94a7 |
completed | April 19, 2026, 7:53 p.m. |
Created at: April 10, 2026, 1:45 p.m.