Triple
T12562176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MacTeX |
E295376
|
entity |
| Predicate | includes |
P1393
|
FINISHED |
| Object | Ghostscript |
E155913
|
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: Ghostscript | Statement: [MacTeX, includes, Ghostscript]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ghostscript Context triple: [MacTeX, includes, Ghostscript]
-
A.
Ghostscript
chosen
Ghostscript is a suite of software that interprets and renders PostScript and PDF files, widely used for document viewing, printing, and conversion.
-
B.
PostScript
PostScript is a page description and programming language widely used in desktop publishing and printing to precisely define the layout and appearance of text and graphics.
-
C.
GSview
GSview is a graphical user interface for viewing and managing PostScript and PDF files using the Ghostscript interpreter.
-
D.
PasteScript
PasteScript is a Python-based command-line tool that streamlines creating, managing, and deploying web application projects through reusable templates and scripts.
-
E.
DjVu
DjVu is a digital document format designed for efficiently storing and viewing scanned documents, especially those containing a mix of text, line drawings, and photographs, with high compression and fast web viewing.
- 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_69d6ad9cac2c81908e8a7bed82d1e21d |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95494ae1c81908b9ee14b8ef92a65 |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6558da7e0819086860bfaf394e2d8 |
completed | May 2, 2026, 7:50 p.m. |
Created at: April 8, 2026, 11:48 p.m.