Triple
T8896835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fair Sentencing Act of 2010 |
E211824
|
entity |
| Predicate | previousRatio |
P85119
|
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: [Fair Sentencing Act of 2010, previousRatio, 100:1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: previousRatio Context triple: [Fair Sentencing Act of 2010, previousRatio, 100:1]
-
A.
previousSharedDivision
Indicates that two entities have both belonged to or participated in the same division at some earlier point in time.
-
B.
previousAlignment
Indicates that one entity’s alignment or orientation occurred earlier in time than another’s.
-
C.
previousPeg
Indicates that one peg directly precedes another peg in a defined sequence or ordering.
-
D.
previousDenomination
Indicates that one entity was the earlier or former denomination (name, value, or classification) of another entity in a sequence of denominations.
-
E.
previousMode
Indicates that one mode directly precedes another in a sequence or state transition.
- 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_69ca83918d3081909b326fa3750cb8c8 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6424a8c08190aef2aa2079dd85f1 |
completed | April 1, 2026, 12:17 a.m. |
| PD | Predicate disambiguation | batch_69cc5c2bfb38819083d5eb1af8ccf4d6 |
completed | March 31, 2026, 11:43 p.m. |
| PDg | Predicate description generation | batch_69cc5cffe8ec819084c12770fe0578f2 |
completed | March 31, 2026, 11:47 p.m. |
Created at: March 30, 2026, 6:54 p.m.