Triple
T12899824
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beamer |
E308583
|
entity |
| Predicate | compatibleWith |
P203
|
FINISHED |
| Object | LuaLaTeX |
E308572
|
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: LuaLaTeX | Statement: [Beamer, compatibleWith, LuaLaTeX]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LuaLaTeX Context triple: [Beamer, compatibleWith, LuaLaTeX]
-
A.
LuaTeX
chosen
LuaTeX is an extended TeX-based typesetting engine that embeds the Lua scripting language to provide powerful programmability and advanced typographic features beyond traditional LaTeX workflows.
-
B.
LaTeX
LaTeX is a widely used, high-quality typesetting system particularly popular in academia for producing technical and scientific documents with precise control over layout and mathematical notation.
-
C.
upLaTeX
upLaTeX is a LaTeX format tailored for Japanese typesetting, built on top of upTeX to provide high-quality multilingual document preparation.
-
D.
LaTeX3
LaTeX3 is the next-generation, experimental programming layer and macro system for LaTeX, designed to provide a more consistent, modular, and extensible foundation for document preparation.
-
E.
LaTeX Companion
The LaTeX Companion is a comprehensive reference book that explains advanced LaTeX features, packages, and best practices for typesetting professional-quality documents.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97180ee708190b60a3e58c42f764f |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6d5ef4ba8819099335d155feb01d9 |
completed | May 3, 2026, 4:58 a.m. |
Created at: April 9, 2026, 5:40 p.m.