Triple
T4051929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CCPA |
E84603
|
entity |
| Predicate | statutoryDamagesRange |
P28687
|
FINISHED |
| Object | 100 to 750 USD per consumer per incident |
—
|
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: 100 to 750 USD per consumer per incident | Statement: [CCPA, statutoryDamagesRange, 100 to 750 USD per consumer per incident]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: statutoryDamagesRange Context triple: [CCPA, statutoryDamagesRange, 100 to 750 USD per consumer per incident]
-
A.
statutoryLimitPerIncidentUSD
chosen
Indicates the maximum monetary amount, in U.S. dollars, that is legally allowed to be claimed or paid for a single incident under a specific statute or regulation.
-
B.
approximateNumberAwarded
Indicates the estimated quantity of awards or recognitions given in a particular context or event.
-
C.
legalCharge
Indicates that an authority has formally accused an entity of committing a specific legal offense or violation.
-
D.
broadcastRightsFeeUS
Indicates the amount of money paid in the United States for the rights to broadcast an event or content.
-
E.
economicDamageApprox
Indicates that one entity has caused or is associated with an estimated or approximate amount of economic damage to another entity or system.
- 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_69aed933bec881909edfa28ebb69c634 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb8539148190990468c1429be9dd |
completed | March 9, 2026, 4:55 p.m. |
| PD | Predicate disambiguation | batch_69aef90249e4819095e9e043bc4aa9a6 |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:37 p.m.