Triple
T146095
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lifelong Learning Machines program |
E3332
|
entity |
| Predicate | timeFrame |
P302
|
FINISHED |
| Object | late 2010s |
—
|
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: late 2010s | Statement: [Lifelong Learning Machines program, timeFrame, late 2010s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeFrame Context triple: [Lifelong Learning Machines program, timeFrame, late 2010s]
-
A.
timePeriod
chosen
Indicates the specific span or interval of time during which an event, state, or relationship occurs or is valid.
-
B.
time
Indicates a temporal relationship specifying when an event occurs or how entities are ordered or related in time.
-
C.
timeDescribedAs
Indicates that a specific time or temporal interval is characterized, labeled, or expressed using a particular description or representation.
-
D.
focusPeriod
Indicates the specific time span during which attention, activity, or analysis is concentrated on something.
-
E.
timeNotation
Indicates the specific system or format used to represent and write times (e.g., 12-hour vs 24-hour notation).
- 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_69a252868de4819080e21c9938bfe8b6 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a257ea7eac8190884a53453a9e0dd6 |
completed | Feb. 28, 2026, 2:50 a.m. |
| PD | Predicate disambiguation | batch_69a25656a4fc81908a87678ac3d28f93 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.