Triple
T2066576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eric Yuan |
E45913
|
entity |
| Predicate | previousEmployer |
P1910
|
FINISHED |
| Object | WebEx |
E34167
|
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: WebEx | Statement: [Eric Yuan, previousEmployer, WebEx]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WebEx Context triple: [Eric Yuan, previousEmployer, WebEx]
-
A.
Webex
chosen
Webex is Cisco’s cloud-based suite of video conferencing, online meeting, and team collaboration tools used by businesses and organizations worldwide.
-
B.
Zoho Meeting
Zoho Meeting is an online web conferencing and webinar platform that enables users to host virtual meetings, screen sharing, and remote collaboration.
-
C.
Zoom
Zoom is a built-in macOS accessibility feature that magnifies on-screen content to make it easier for users with low vision to see and interact with their display.
-
D.
NetMeeting
NetMeeting was a Microsoft videoconferencing and collaboration application that enabled voice, video, and data sharing over the internet on Windows systems.
-
E.
Google Meet
Google Meet is a video conferencing service by Google that enables online meetings, voice calls, and screen sharing for individuals, businesses, and educational institutions.
- 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_69a8891b38288190abd572ccad9b6928 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9f236f08190b602d337afb1880f |
completed | March 7, 2026, 5:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae2722b5048190a78978a64167cda2 |
completed | March 9, 2026, 1:49 a.m. |
Created at: March 4, 2026, 7:40 p.m.