Triple
T263650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federal Insurance Contributions Act taxes |
E5806
|
entity |
| Predicate | hasRateType |
P9385
|
FINISHED |
| Object | flat rate |
—
|
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: flat rate | Statement: [Federal Insurance Contributions Act taxes, hasRateType, flat rate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRateType Context triple: [Federal Insurance Contributions Act taxes, hasRateType, flat rate]
-
A.
hasRecordType
Indicates that an entity is associated with or classified under a specific type or category of record.
-
B.
hasYearType
Indicates a relationship where an entity is associated with a specific classification or category of year (such as calendar, fiscal, academic, or other year type).
-
C.
hasMonthType
Indicates that something is associated with, classified by, or characterized as a particular type or category of month.
-
D.
hasServiceType
Indicates that an entity is associated with or categorized by a particular type of service.
-
E.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
- 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_69a258dd8ea08190ac554a1cc8dfd8c3 |
completed | Feb. 28, 2026, 2:54 a.m. |
| NER | Named-entity recognition | batch_69a25d8e809881908a58c9a4e3ba07c3 |
completed | Feb. 28, 2026, 3:14 a.m. |
| PD | Predicate disambiguation | batch_69a25b6e07748190834022a65ba6d803 |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25d0ec71081908478c800be4f7bb0 |
completed | Feb. 28, 2026, 3:12 a.m. |
Created at: Feb. 28, 2026, 2:55 a.m.