Triple
T452082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Microsoft 365 |
E7151
|
entity |
| Predicate | includes |
P1393
|
FINISHED |
| Object | Teams |
E5699
|
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: Teams | Statement: [Microsoft 365, includes, Teams]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teams Context triple: [Microsoft 365, includes, Teams]
-
A.
Teams
chosen
Teams is Microsoft's cloud-based collaboration and communication platform that integrates chat, video meetings, file sharing, and app integrations for organizations.
-
B.
Team Gray
Team Gray was a robotics team that gained recognition for competing in DARPA’s pioneering autonomous vehicle Grand Challenge.
-
C.
Red and Blue Crew
Red and Blue Crew is the official student cheering section that supports the University of Pennsylvania’s athletic teams, especially at Penn Quakers football games.
-
D.
Team KAIST
Team KAIST is a South Korean robotics research team from the Korea Advanced Institute of Science and Technology renowned for developing advanced humanoid robots and achieving top honors in international robotics competitions.
-
E.
easyGroup
easyGroup is a British private investment company best known for owning the "easy" family of brands, including the low-cost airline easyJet.
- 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_69a2e7e4676c81909ea0dbdecac0687c |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ef854f7481909dc2207faf0327ec |
completed | Feb. 28, 2026, 1:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a44802e858819081a0b5b98bb25bce |
completed | March 1, 2026, 2:06 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.