Triple
T4720040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aklanon |
E104740
|
entity |
| Predicate | hasVoiceSystem |
P59152
|
FINISHED |
| Object | focus system |
—
|
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: focus system | Statement: [Aklanon, hasVoiceSystem, focus system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVoiceSystem Context triple: [Aklanon, hasVoiceSystem, focus system]
-
A.
hasVoiceIn
Indicates that an entity participates by providing a voice role or vocal performance in another entity, such as a work, production, or recording.
-
B.
usesVoiceAssistant
Indicates that an entity regularly operates or interacts with a voice-controlled digital assistant.
-
C.
hasSpeech
Indicates that an entity produces, delivers, or is associated with a spoken utterance or verbal expression.
-
D.
hasSpeakerType
Indicates that an entity functions in a particular role or category as a speaker (e.g., narrator, character, announcer) within a given context.
-
E.
speakerConfiguration
Indicates how speakers are arranged or assigned within an audio or communication setup.
- 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_69bd43ec4a348190bc41afae43375e71 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd680beb508190b3d74e20e1c64405 |
completed | March 20, 2026, 3:30 p.m. |
| PD | Predicate disambiguation | batch_69bd621ddcd88190903288566f5e5dab |
completed | March 20, 2026, 3:05 p.m. |
| PDg | Predicate description generation | batch_69bd680b06e881908de87edf815f3cc0 |
completed | March 20, 2026, 3:30 p.m. |
Created at: March 20, 2026, 1:18 p.m.