Triple
T16604449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Cabinet (Australia) |
E403413
|
entity |
| Predicate | meetingMode |
P3082
|
FINISHED |
| Object | in-person |
—
|
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: in-person | Statement: [National Cabinet (Australia), meetingMode, in-person]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: meetingMode Context triple: [National Cabinet (Australia), meetingMode, in-person]
-
A.
meetingType
chosen
Indicates the specific category or format of a meeting that characterizes how it is organized or conducted.
-
B.
meetingState
Indicates the current status or phase of a meeting within its overall lifecycle (e.g., scheduled, in progress, or completed).
-
C.
meetingLevel
Indicates the degree or intensity of interaction or engagement between participants in a meeting or meeting-like context.
-
D.
meetingContext
Indicates the situational setting, circumstances, or background conditions in which a meeting takes place.
-
E.
meetingNumber
Indicates the specific numerical identifier assigned to distinguish one meeting from others.
- 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_69d883880d0c81908b5fcd454e767b60 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3608ff1a481909084e7ad984b0f95 |
completed | April 18, 2026, 10:44 a.m. |
| PD | Predicate disambiguation | batch_69e296aabc508190b3836a91b49113ad |
completed | April 17, 2026, 8:23 p.m. |
Created at: April 10, 2026, 5:17 a.m.