Triple
T4883266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EB |
E109379
|
entity |
| Predicate | mainMeeting |
P7708
|
FINISHED |
| Object | January session |
—
|
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: January session | Statement: [EB, mainMeeting, January session]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainMeeting Context triple: [EB, mainMeeting, January session]
-
A.
meetingType
Indicates the specific category or format of a meeting that characterizes how it is organized or conducted.
-
B.
meeting
Indicates that two or more entities come together at the same time and place for discussion, decision-making, or coordinated activity.
-
C.
meetingContext
Indicates the situational setting, circumstances, or background conditions in which a meeting takes place.
-
D.
notableMeeting
Indicates that two or more entities participated together in a meeting considered significant or noteworthy in some context.
-
E.
hasPrimaryMeeting
chosen
Indicates that an entity is associated with its main or most important meeting, distinguishing it from other meetings it may have.
- 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_69bd440f71348190b99938e59fb7f9a1 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ddfff0c81908fb148a6f6508334 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c2be5e881909f6ec9c3bcde49f3 |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:27 p.m.