Triple
T27175566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Carolina 401(k) and 457 retirement savings plans |
E683033
|
entity |
| Predicate | contributionFrequency |
P177301
|
FINISHED |
| Object | each pay period |
—
|
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: each pay period | Statement: [North Carolina 401(k) and 457 retirement savings plans, contributionFrequency, each pay period]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: contributionFrequency Context triple: [North Carolina 401(k) and 457 retirement savings plans, contributionFrequency, each pay period]
-
A.
dealFrequency
Indicates how often a particular deal, transaction, or agreement occurs within a given period.
-
B.
awardedFrequency
Indicates how often an award or recognition is given within a specified time period.
-
C.
performedFrequency
Indicates how often an action or activity is carried out within a given time period.
-
D.
seriesFrequency
Indicates how often the events or items in a recurring series occur over time.
-
E.
serviceFrequencyType
Indicates how often a service occurs or is scheduled within a given time period.
- 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_69eefad086808190ab89816c0c300476 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f6fb93224881908fc66fe76115fcdb |
completed | May 3, 2026, 7:38 a.m. |
| PD | Predicate disambiguation | batch_69f6f969b4cc8190afb473a2d8b110bc |
completed | May 3, 2026, 7:29 a.m. |
| PDg | Predicate description generation | batch_69f6fb17d5ec81909091e37e1ddbe577 |
completed | May 3, 2026, 7:36 a.m. |
Created at: April 27, 2026, 9:25 a.m.