Triple
T19754913
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snow Crash |
E474477
|
entity |
| Predicate | setIn |
P1393
|
FINISHED |
| Object | Metaverse |
—
|
NE NERFINISHED |
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: Metaverse | Statement: [Snow Crash, setIn, Metaverse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Metaverse Context triple: [Snow Crash, setIn, Metaverse]
-
A.
Metaverse
chosen
The Metaverse is a collective virtual shared space—popularized by Neal Stephenson’s novel "Snow Crash"—envisioned as an immersive, persistent digital world where users interact through avatars for work, play, and socialization.
-
B.
Virtuality
"Virtuality" is a 2009 science fiction television pilot directed by Peter Berg about a spaceship crew testing a virtual reality system during a deep-space mission.
-
C.
Anteverse
The Anteverse is a mysterious alternate dimension or universe that serves as a key otherworldly setting in speculative fiction narratives.
-
D.
Omniverse View
Omniverse View is an NVIDIA Omniverse application for high-fidelity, real-time visualization and review of 3D scenes and virtual worlds.
-
E.
VR
VR is the abbreviation used for the Volunteer Reserves, the part-time volunteer component of the British Armed Forces.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51940a0819087bd2996f98da668 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6529dada081909c5b4d65247c6032 |
completed | April 20, 2026, 4:21 p.m. |
Created at: April 10, 2026, 1:48 p.m.