Triple
T1542733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Do Not Pay system |
E32905
|
entity |
| Predicate | users |
P14200
|
FINISHED |
| Object | federal program integrity staff |
—
|
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: federal program integrity staff | Statement: [Do Not Pay system, users, federal program integrity staff]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: users Context triple: [Do Not Pay system, users, federal program integrity staff]
-
A.
userBase
Indicates that one entity serves as the primary or foundational group of users associated with another entity.
-
B.
user
Indicates a relationship where an entity actively operates, controls, or interacts with another entity, typically as the primary agent or consumer of its function.
-
C.
dataUsers
Indicates a relationship where certain entities use, access, or consume specific data.
-
D.
servesUsers
chosen
Indicates that an entity provides services, functionality, or benefits to one or more users.
-
E.
userCount
Indicates the number of users associated with or involved in a given context or entity.
- 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_69a885ed29088190a3c2d5a3d100c16e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa95c1a2948190a2b98469afec1a7d |
completed | March 6, 2026, 8:52 a.m. |
| PD | Predicate disambiguation | batch_69a907b2453c8190a41f6b88c8217d1e |
completed | March 5, 2026, 4:33 a.m. |
Created at: March 4, 2026, 7:26 p.m.