Triple
T1605575
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Social Security wage base limit |
E34495
|
entity |
| Predicate | isAdjustedFrequency |
P28499
|
FINISHED |
| Object | annually |
—
|
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: annually | Statement: [Social Security wage base limit, isAdjustedFrequency, annually]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isAdjustedFrequency Context triple: [Social Security wage base limit, isAdjustedFrequency, annually]
-
A.
hasFrequencyCategory
chosen
Indicates that something is associated with a particular classification of how often it occurs or is used.
-
B.
isFrequently
Indicates that an action, state, or relationship occurs often or with high regularity between the related entities.
-
C.
isFrequentlyAdapted
Indicates that a work or source material is often transformed or re-created into new formats or versions, such as films, plays, or other media.
-
D.
usesFrequency
Indicates that one entity employs or operates another entity at a specified rate, interval, or number of occurrences over time.
-
E.
hasFrequencyNote
Indicates that something is associated with a specific note describing how often it occurs or is repeated.
- 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_69a885fea6a481909fe83ba6441f1774 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a93fa6926081908bc78d15c0be3185 |
completed | March 5, 2026, 8:32 a.m. |
| PD | Predicate disambiguation | batch_69a907c35f848190a2428c52e81d013e |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.