Triple
T1901294
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anti-Drug Abuse Act of 1986 |
E37693
|
entity |
| Predicate | increasedPenaltiesFor |
P7907
|
FINISHED |
| Object | drug trafficking near schools |
—
|
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: drug trafficking near schools | Statement: [Anti-Drug Abuse Act of 1986, increasedPenaltiesFor, drug trafficking near schools]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: increasedPenaltiesFor Context triple: [Anti-Drug Abuse Act of 1986, increasedPenaltiesFor, drug trafficking near schools]
-
A.
penaltyProvision
Indicates that a rule, contract, or law includes a clause specifying a punishment or sanction for non-compliance or violation.
-
B.
punishedBy
Indicates that an entity receives punishment administered by another entity.
-
C.
hasPunishment
Indicates that an entity is subject to a specified penalty, sanction, or adverse consequence as a result of some action, condition, or rule.
-
D.
penaltyForRevealingSecrets
Indicates that an entity incurs a punishment or negative consequence as a result of disclosing confidential or secret information.
-
E.
increases
chosen
Indicates that one entity causes another entity’s value, level, or intensity to become larger or higher.
- 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_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. |
Created at: March 4, 2026, 7:35 p.m.