Triple
T18173031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Airtime |
E435078
|
entity |
| Predicate | hasInteractionModel |
P23418
|
FINISHED |
| Object | real-time synchronous communication |
—
|
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: real-time synchronous communication | Statement: [Airtime, hasInteractionModel, real-time synchronous communication]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInteractionModel Context triple: [Airtime, hasInteractionModel, real-time synchronous communication]
-
A.
userInteractionModel
chosen
Indicates how a user is expected to interact with a system, defining the style, rules, or pattern of those interactions.
-
B.
hasInteraction
Indicates that there is some form of interaction or mutual action occurring between the related entities.
-
C.
hasLanguageModel
Indicates that an entity possesses, uses, or is associated with a particular language model.
-
D.
hasInteractiveActivity
Indicates that an entity includes or is associated with an activity that requires active user participation or engagement.
-
E.
hasInteractionEffects
Indicates that one entity’s presence, action, or state alters, influences, or modifies the behavior, effect, or outcome associated with another entity.
- 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_69d8b90b7a188190b3fc7b8d4a6cd20a |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4df56d0a88190af3f407d2a3bb74f |
completed | April 19, 2026, 1:57 p.m. |
| PD | Predicate disambiguation | batch_69e4331baeb88190b21f50a98c36c78e |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:30 a.m.