Triple
T7088872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Echo Dot |
E165141
|
entity |
| Predicate | hasMicrophoneArray |
P22669
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Echo Dot, hasMicrophoneArray, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMicrophoneArray Context triple: [Echo Dot, hasMicrophoneArray, yes]
-
A.
hasMicrophones
chosen
Indicates that one entity possesses, includes, or is equipped with one or more microphones.
-
B.
supportsMultiRoomAudio
Indicates that the subject is capable of playing synchronized audio across multiple rooms or speakers simultaneously.
-
C.
supportsAudioReturnChannel
Indicates that one entity is capable of handling an audio return channel from another entity, allowing audio to be sent back over the same connection.
-
D.
haveRichVoiceSystem
Indicates that an entity possesses a voice system characterized by richness, such as depth, fullness, or expressive quality.
-
E.
hasSpeechLevels
Indicates that a language, dialect, or communicative system distinguishes different levels or styles of speech used according to social context, formality, or relative status between speakers.
- 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_69c6887d98408190912b9580666b0c1d |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e52ec0348190ac090c2fee3edfb8 |
completed | March 27, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69c6e1c172148190bf290c07bf579d1f |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:41 p.m.