Triple
T30384072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sony WI-C200 |
E772900
|
entity |
| Predicate | supportsMultipoint |
P207259
|
FINISHED |
| Object | false |
—
|
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: false | Statement: [Sony WI-C200, supportsMultipoint, false]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsMultipoint Context triple: [Sony WI-C200, supportsMultipoint, false]
-
A.
supportsMultiRoomAudio
Indicates that the subject is capable of playing synchronized audio across multiple rooms or speakers simultaneously.
-
B.
supportsMultipleTerminals
Indicates that an entity is capable of handling or operating with more than one terminal or endpoint simultaneously.
-
C.
supportsDeviceCount
Indicates the number of devices that a system, service, or component is capable of supporting concurrently.
-
D.
supportsMulticast
Indicates that an entity is capable of handling or enabling multicast communication, where data is transmitted from one sender to multiple receivers simultaneously.
-
E.
supportsMultipleWindows
Indicates that the subject can handle or display more than one window or view simultaneously.
- F. None of above. chosen
Provenance (4 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_69f2248e3444819081b05712dc6873de |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e2fac0819089b522db3260028c |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7ee0388190a29faeb5cdb0950a |
completed | May 12, 2026, 7:16 p.m. |
Created at: April 29, 2026, 8:01 p.m.