Triple
T7089166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fire tablet |
E165147
|
entity |
| Predicate | introducedAs |
P513
|
FINISHED |
| Object | Kindle Fire |
E165151
|
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: Kindle Fire | Statement: [Fire tablet, introducedAs, Kindle Fire]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kindle Fire Context triple: [Fire tablet, introducedAs, Kindle Fire]
-
A.
Kindle
Kindle is Amazon’s line of portable e-readers designed primarily for reading digital books and other electronic publications.
-
B.
Amazon Fire tablet
chosen
The Amazon Fire tablet is a line of budget-friendly Android-based tablets by Amazon, designed for media consumption, reading, and integration with Amazon’s digital services.
-
C.
Nook e-reader
The Nook e-reader is Barnes & Noble’s line of electronic reading devices designed for purchasing, downloading, and reading digital books and other publications.
-
D.
Kindle Cloud Reader
Kindle Cloud Reader is a web-based application by Amazon that lets users read and manage their Kindle ebooks directly in a browser without needing a dedicated device or app.
-
E.
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.
- 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_69c6887d98408190912b9580666b0c1d |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e52ec0348190ac090c2fee3edfb8 |
completed | March 27, 2026, 8:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c79c924ac88190a237d2e0ac505d51 |
completed | March 28, 2026, 9:17 a.m. |
Created at: March 27, 2026, 2:41 p.m.