Triple
T15513826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EPUB |
E368779
|
entity |
| Predicate | version |
P3286
|
FINISHED |
| Object | EPUB 2 |
E368779
|
NE 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: EPUB 2 | Statement: [EPUB, version, EPUB 2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: EPUB 2 Context triple: [EPUB, version, EPUB 2]
-
A.
EPUB
chosen
EPUB is a widely used open e-book file format designed for reflowable digital publications that can adapt to different screen sizes and devices.
-
B.
Adobe Digital Editions
Adobe Digital Editions is an ebook management and reading software application from Adobe that supports DRM-protected EPUB and PDF files across multiple devices.
-
C.
KPUB
KPUB is the ICAO airport code for Pueblo Memorial Airport, a public airport serving Pueblo, Colorado, in the United States.
-
D.
Kobo e-readers
Kobo e-readers are a line of digital reading devices known for their wide format support, integration with public libraries, and openness compared to many competing platforms.
-
E.
Rocket eBook at NuvoMedia
Rocket eBook at NuvoMedia was one of the first commercially produced handheld electronic book readers, co-developed by engineer and entrepreneur Marc Tarpenning in the late 1990s.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d85a1794cc8190b0b428716296e63e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e04031e62c8190953b61207142af15 |
completed | April 16, 2026, 1:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d4edee481908382ca5cd266f7b0 |
completed | May 9, 2026, 1:57 p.m. |
Created at: April 10, 2026, 4:02 a.m.