Triple
T1901289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anti-Drug Abuse Act of 1986 |
E37693
|
entity |
| Predicate | crackToPowderCocaineSentencingRatio |
P33770
|
FINISHED |
| Object | 100:1 |
—
|
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: 100:1 | Statement: [Anti-Drug Abuse Act of 1986, crackToPowderCocaineSentencingRatio, 100:1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: crackToPowderCocaineSentencingRatio Context triple: [Anti-Drug Abuse Act of 1986, crackToPowderCocaineSentencingRatio, 100:1]
-
A.
sentencedTo
Indicates that an authority has officially assigned a specific punishment or penalty to an entity, typically as the outcome of a legal or disciplinary process.
-
B.
numberOfConvictions
Indicates the count of times an entity has been formally found guilty of an offense.
-
C.
durationOfImprisonment
Indicates the length of time that an entity is or was held in imprisonment.
-
D.
numberSentencedToLifeImprisonment
Indicates the number of individuals who have been given a sentence of life imprisonment as a legal punishment.
-
E.
isCriminalizedIn
Indicates that a specific behavior, action, or condition is prohibited and subject to legal penalties within a particular jurisdiction or legal system.
- 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_69a8861be7148190a680937ec451a304 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb34d94fc8190a5bf1e582c77c725 |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abafe9f8b0819086d8f6288511c66d |
completed | March 7, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69abb34c4a64819096e12b152b84c334 |
completed | March 7, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:35 p.m.