Triple
T11121291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kevin McGibbon |
E263021
|
entity |
| Predicate | screenTimeEra |
P97662
|
FINISHED |
| Object | 1980s |
—
|
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: 1980s | Statement: [Kevin McGibbon, screenTimeEra, 1980s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: screenTimeEra Context triple: [Kevin McGibbon, screenTimeEra, 1980s]
-
A.
screenTimeFunction
Indicates a functional relationship that maps an entity (such as a user, device, or app) to the amount of time its screen is active or in use.
-
B.
screenTimeFocus
Indicates the amount or proportion of time an entity’s attention or activity is concentrated on a particular screen or digital display.
-
C.
hasScreenTimeIn
Indicates that an entity appears on screen for a certain duration within a specified audiovisual work or segment.
-
D.
showsTimePeriod
Indicates that one entity presents or displays a specific span or interval of time associated with another entity.
-
E.
screenTimeImportance
Indicates how important or significant the amount of time spent using screens or digital devices is considered in a given context.
- 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_69d6aa9b46cc8190b19f9f0cc45bf322 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79af911b881908168f23a4918231c |
completed | April 9, 2026, 12:26 p.m. |
| PD | Predicate disambiguation | batch_69d7441efe948190ae4ab020121e169d |
completed | April 9, 2026, 6:15 a.m. |
| PDg | Predicate description generation | batch_69d75112eb1c8190ba1dd51e42e8a7e8 |
completed | April 9, 2026, 7:11 a.m. |
Created at: April 8, 2026, 9:28 p.m.