Triple
T5844861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Jung |
E129682
|
entity |
| Predicate | hasCauseOfNotoriety |
P22
|
FINISHED |
| Object | importing cocaine into the United States |
—
|
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: importing cocaine into the United States | Statement: [George Jung, hasCauseOfNotoriety, importing cocaine into the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCauseOfNotoriety Context triple: [George Jung, hasCauseOfNotoriety, importing cocaine into the United States]
-
A.
notableFor
chosen
Indicates that an entity is especially recognized or distinguished for a particular quality, achievement, characteristic, or role.
-
B.
hasBeenSubjectOf
Indicates that an entity has previously been the focus or target of a particular action, process, or investigation.
-
C.
fameFor
Indicates that one entity is widely known or recognized specifically because of, or in connection with, another entity.
-
D.
hasNotableReputation
Indicates that an entity is widely recognized or distinguished for a particular quality, achievement, or characteristic.
-
E.
hasNamesakeNotability
Indicates that one entity is notable or recognized specifically because it shares the same name as another 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_69c0084bd31c8190a796bb6284845e83 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03c9239e08190bff7ef2bd6d21ae0 |
completed | March 22, 2026, 7:01 p.m. |
| PD | Predicate disambiguation | batch_69c0334412388190bc594794ec5754f9 |
completed | March 22, 2026, 6:21 p.m. |
Created at: March 22, 2026, 3:55 p.m.