Triple
T8650104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MacBook Air (M2, 2022) |
E205076
|
entity |
| Predicate | microphone |
P22669
|
FINISHED |
| Object | three-mic array |
—
|
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: three-mic array | Statement: [MacBook Air (M2, 2022), microphone, three-mic array]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: microphone Context triple: [MacBook Air (M2, 2022), microphone, three-mic array]
-
A.
hasMicrophones
chosen
Indicates that one entity possesses, includes, or is equipped with one or more microphones.
-
B.
soundRecordingBy
Indicates that a sound recording was created, performed, or produced by a particular agent (such as an artist, band, or producer).
-
C.
audioChip
Indicates that one entity functions as, or contains, an audio processing chip in relation to another entity.
-
D.
recordingOf
Indicates that one entity is an audio or video capture or performance that documents, represents, or preserves another entity (such as a work, event, or expression).
-
E.
speakerConfiguration
Indicates how speakers are arranged or assigned within an audio or communication setup.
- 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_69ca834e56848190abb0eeaec9dedd32 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc4813d0548190b203e594acc38c8f |
completed | March 31, 2026, 10:17 p.m. |
| PD | Predicate disambiguation | batch_69cc45619460819091e83ffdec99c865 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:29 p.m.