Triple
T7168819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prescription Drug User Fee Act of 1992 |
E167139
|
entity |
| Predicate | typeOfFee |
P37869
|
FINISHED |
| Object | application fee |
—
|
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: application fee | Statement: [Prescription Drug User Fee Act of 1992, typeOfFee, application fee]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfFee Context triple: [Prescription Drug User Fee Act of 1992, typeOfFee, application fee]
-
A.
feeType
chosen
Indicates the specific category or classification of a fee associated with a transaction, service, or obligation.
-
B.
tuitionFeeType
Indicates the category or structure of tuition fees that applies to an entity (such as full-time, part-time, in-state, out-of-state, or other fee types).
-
C.
chargeType
Indicates the category or nature of a charge applied in a transaction or interaction between entities (e.g., fee type, billing classification, or legal charge type).
-
D.
fareType
Indicates the category or class of fare (such as standard, discounted, or promotional) that applies to a given trip, ticket, or pricing instance.
-
E.
feeUnit
Indicates the unit of measurement or currency in which a fee amount is expressed.
- 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_69c68888c10c819095e0383020225758 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e85c606081909f76d76fc5b90bc8 |
completed | March 27, 2026, 8:28 p.m. |
| PD | Predicate disambiguation | batch_69c6e1cd5c948190a9113b23f7308c21 |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:48 p.m.