Triple
T22245599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Plan N |
E549831
|
entity |
| Predicate | benefitsDoNot |
P66312
|
FINISHED |
| Object | change from year to year unless federal law changes |
—
|
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: change from year to year unless federal law changes | Statement: [Plan N, benefitsDoNot, change from year to year unless federal law changes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: benefitsDoNot Context triple: [Plan N, benefitsDoNot, change from year to year unless federal law changes]
-
A.
benefitsNot
chosen
Indicates that one entity does not provide an advantage, help, or positive effect to another entity.
-
B.
benefitsAre
Indicates that certain advantages, gains, or positive outcomes are possessed by or accrue to a particular entity or group.
-
C.
benefits
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or action.
-
D.
hasNotableBenefaction
Indicates that an entity has provided a significant or noteworthy benefit, donation, or contribution to another entity or cause.
-
E.
benefitsCause
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or cause.
- 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_69e11e41d9408190bd770cf282e22753 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f13217c9f88190aa2ce7d644b57739 |
completed | April 28, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69e72fe1e0cc8190bd13cff2a0846225 |
completed | April 21, 2026, 8:05 a.m. |
Created at: April 16, 2026, 8:38 p.m.