Triple
T9003524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Messages |
E215089
|
entity |
| Predicate | supportsProtocol |
P203
|
FINISHED |
| Object | iMessage |
E122390
|
NE 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: iMessage | Statement: [Messages, supportsProtocol, iMessage]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: iMessage Context triple: [Messages, supportsProtocol, iMessage]
-
A.
iMessage
chosen
iMessage is Apple’s encrypted instant messaging service that enables text, media, and rich communication features between users of Apple devices.
-
B.
FaceTime
FaceTime is Apple’s proprietary video and audio calling service that enables real-time communication across its devices and platforms.
-
C.
IMS
IMS (IP Multimedia Subsystem) is a standardized architectural framework for delivering IP-based multimedia services over mobile and fixed networks.
-
D.
IMS
IMS is a leading biomedical research institute focused on understanding metabolic diseases such as obesity and diabetes.
-
E.
IMS
IMS is IBM's hierarchical database and transaction management system widely used on mainframe platforms for high-volume, mission-critical applications.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca83a12d648190b1e4fe11e8a31890 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6959497c8190a748c78504dd2eb6 |
completed | April 1, 2026, 12:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfeb5b88d48190b684c43cc88d8438 |
completed | April 3, 2026, 4:31 p.m. |
Created at: March 30, 2026, 7:05 p.m.