Triple
T2798266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regulation Crowdfunding |
E53089
|
entity |
| Predicate | intermediaryTypes |
P32144
|
FINISHED |
| Object | registered broker-dealer |
—
|
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: registered broker-dealer | Statement: [Regulation Crowdfunding, intermediaryTypes, registered broker-dealer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: intermediaryTypes Context triple: [Regulation Crowdfunding, intermediaryTypes, registered broker-dealer]
-
A.
intermediaryType
chosen
Indicates the specific role or category of intermediary involved in facilitating the relationship or transaction between entities.
-
B.
isInterchangeWith
Indicates that two entities can be substituted or exchanged for one another without loss of function, value, or compatibility.
-
C.
beneficiaryType
Indicates the type or category of beneficiary that receives or is intended to receive the benefit or outcome of an action or resource.
-
D.
settlementType
Indicates the specific kind or category of human settlement an entity represents, such as a city, village, town, or hamlet.
-
E.
lenderType
Indicates the classification or category of the lender involved in a lending relationship (e.g., bank, individual, institution).
- 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_69ab495a90788190941b6917e1eca3a6 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abddf204148190a53f3f30d645d94c |
completed | March 7, 2026, 8:12 a.m. |
| PD | Predicate disambiguation | batch_69abdd040f9481908e9c7a2df88ea1ae |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:58 p.m.