Triple
T19751885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Life Plus 99 Years |
E474397
|
entity |
| Predicate | timeInLifeCovered |
P3058
|
FINISHED |
| Object | decades in prison |
—
|
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: decades in prison | Statement: [Life Plus 99 Years, timeInLifeCovered, decades in prison]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeInLifeCovered Context triple: [Life Plus 99 Years, timeInLifeCovered, decades in prison]
-
A.
timePeriodCoveredTo
chosen
Indicates the span or duration of time that is encompassed, addressed, or relevant to a given subject or entity.
-
B.
timeHorizonCovered
Indicates the span or duration of time that is encompassed or addressed by something (such as a plan, forecast, or agreement).
-
C.
coversYearsTo
Indicates a temporal relationship where one entity spans, includes, or extends up to a specified year or range of years represented by the other entity.
-
D.
hasCareerSpanCoverage
Indicates that one entity’s coverage, record, or data extends across the full duration of another entity’s career span.
-
E.
durationInYears
Indicates the length of time associated with something, measured in whole or fractional years.
- 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_69d8e51940a0819087bd2996f98da668 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65299a7048190a9b22307ac06bd03 |
completed | April 20, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69e5305016e08190b9561a96baecb0b8 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:48 p.m.