Triple
T28296009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MP/M |
E713571
|
entity |
| Predicate | supportsUserCount |
P31535
|
FINISHED |
| Object | multiple concurrent users |
—
|
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: multiple concurrent users | Statement: [MP/M, supportsUserCount, multiple concurrent users]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsUserCount Context triple: [MP/M, supportsUserCount, multiple concurrent users]
-
A.
supportsMultiuser
chosen
Indicates that the subject is capable of handling or enabling simultaneous use by multiple users.
-
B.
supportsDeviceCount
Indicates the number of devices that a system, service, or component is capable of supporting concurrently.
-
C.
supportsUsers
Indicates that one entity provides assistance, functionality, or compatibility for the users associated with another entity.
-
D.
userCount
Indicates the number of users associated with or involved in a given context or entity.
-
E.
supportsAudienceSize
Indicates that one entity is capable of accommodating or handling an audience of a specified size.
- 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_69efb524ab688190a1ce7ee7c9520932 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_6a00818b20a881909fbf3bb33dcf7029 |
completed | May 10, 2026, 1 p.m. |
| PD | Predicate disambiguation | batch_6a0080f76f588190a933238861243d1a |
completed | May 10, 2026, 12:58 p.m. |
Created at: April 27, 2026, 11:32 p.m.