Triple
T8917399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa's workshop |
E212325
|
entity |
| Predicate | timeOfMainActivity |
P10350
|
FINISHED |
| Object | months before Christmas |
—
|
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: months before Christmas | Statement: [Santa's workshop, timeOfMainActivity, months before Christmas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeOfMainActivity Context triple: [Santa's workshop, timeOfMainActivity, months before Christmas]
-
A.
screenTimeFocus
Indicates the amount or proportion of time an entity’s attention or activity is concentrated on a particular screen or digital display.
-
B.
timeToControl
Indicates the amount of time required for an entity to gain or establish control over another entity, process, or situation.
-
C.
timeStatus
Indicates the temporal state or condition of an event or entity relative to a reference time (e.g., past, present, future, ongoing, or scheduled).
-
D.
activityTime
chosen
Indicates the time period during which an activity occurs or is scheduled to take place.
-
E.
hasScreenTimeIn
Indicates that an entity appears on screen for a certain duration within a specified audiovisual work or segment.
- 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_69ca8393b1808190bd4336787ffa2c40 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc66120eb08190913ab6c42f26ffb8 |
completed | April 1, 2026, 12:25 a.m. |
| PD | Predicate disambiguation | batch_69cc5ed0ef3c81908cc69eac852ee12a |
completed | March 31, 2026, 11:54 p.m. |
Created at: March 30, 2026, 6:56 p.m.