Triple
T38635145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Microsoft 365 Personal |
E937556
|
entity |
| Predicate | numberOfLicensedUsers |
P22398
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Microsoft 365 Personal, numberOfLicensedUsers, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfLicensedUsers Context triple: [Microsoft 365 Personal, numberOfLicensedUsers, 1]
-
A.
maximumNumberOfUsers
Indicates the highest allowable or supported number of users associated with or participating in a given context or system.
-
B.
hasLicensingMetric
Indicates that one entity uses another entity as the metric or basis for determining licensing terms or conditions.
-
C.
userCount
chosen
Indicates the number of users associated with or involved in a given context or entity.
-
D.
licenseUsed
Indicates that a particular license has been applied to or is being utilized for a specific resource, activity, or entity.
-
E.
usesLicensingModel
Indicates that one entity employs or applies a particular licensing model in its operations or offerings.
- 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_69f76ed5ca3c81909288f61fbf37b359 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a2026248190b894436a578d79ac |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:32 p.m.