Triple
T9249931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | macOS Catalina |
E222295
|
entity |
| Predicate | includedApplication |
P1393
|
FINISHED |
| Object |
E41444
|
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: Mail | Statement: [macOS Catalina, includedApplication, Mail]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mail Context triple: [macOS Catalina, includedApplication, Mail]
-
A.
Mail
Mail is a Flask extension class that provides a simple interface for sending email messages from Flask web applications.
-
B.
Mail
chosen
Mail is Apple’s built-in email client application for macOS, used to send, receive, and manage email accounts.
-
C.
MAIL
MAIL is the stock ticker symbol for Mail.ru Group, a major Russian internet and online services company.
-
D.
InMail
InMail is LinkedIn’s premium messaging tool that lets users directly contact other members they’re not connected with on the platform.
-
E.
Correo Central
Correo Central is a Buenos Aires Underground station located in the city’s central area, serving as a key access point to nearby government and cultural buildings.
- 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_69ca841d2b18819089f9faf5b2c2aec0 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd05f7e9848190939f9199d0c1a572 |
completed | April 1, 2026, 11:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d077fed7888190a5d36bc2ee4c2bd2 |
completed | April 4, 2026, 2:31 a.m. |
Created at: March 30, 2026, 7:31 p.m.