Triple
T31218667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Virtual reality systems |
E795939
|
entity |
| Predicate | oftenUseDevice |
P9919
|
FINISHED |
| Object | head-mounted display |
—
|
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: head-mounted display | Statement: [Virtual reality systems, oftenUseDevice, head-mounted display]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oftenUseDevice Context triple: [Virtual reality systems, oftenUseDevice, head-mounted display]
-
A.
oftenUse
Indicates that one entity frequently or regularly uses, employs, or utilizes another entity.
-
B.
usesDevice
chosen
Indicates that one entity operates, employs, or relies on a particular device to perform an action or achieve a purpose.
-
C.
usedAsDeviceFor
Indicates that one entity functions as a tool, instrument, or device for performing an action or achieving a particular purpose for another entity.
-
D.
frequentUseCase
Indicates a situation, scenario, or pattern of use that occurs regularly or more often than others in relation to the subject.
-
E.
usesFrequency
Indicates that one entity employs or operates another entity at a specified rate, interval, or number of occurrences over time.
- 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_69f224d9d52c8190a61f68ded37fa755 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f7be53890081909b1d93f30a8f31c6 |
completed | May 3, 2026, 9:29 p.m. |
| PD | Predicate disambiguation | batch_69f7bccacbac8190978976324c67db28 |
completed | May 3, 2026, 9:23 p.m. |
Created at: April 29, 2026, 9:10 p.m.