Triple
T2752659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TeX |
E61023
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | METAPOST |
E30048
|
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: METAPOST | Statement: [TeX, influenced, METAPOST]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: METAPOST Context triple: [TeX, influenced, METAPOST]
-
A.
METAFONT
METAFONT is a font description and rasterization system created by Donald Knuth for designing and generating bitmap fonts, particularly for use with the TeX typesetting system.
-
B.
PostScript
chosen
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.
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.
-
D.
Font’s Point
Font’s Point is a scenic overlook in California’s Anza-Borrego Desert famed for its sweeping sunrise views over the eroded badlands.
-
E.
Adobe PageMaker
Adobe PageMaker was one of the first widely used desktop publishing applications, popular in the 1980s and 1990s for creating professional-quality printed documents such as brochures, newsletters, and books.
- 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_69ab4b7a85bc819094a349b84beb1f2c |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb6d08088190b489de15a120ba3f |
completed | March 7, 2026, 8:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afbbdb43d481909bf4e61840979c0a |
completed | March 10, 2026, 6:36 a.m. |
Created at: March 6, 2026, 9:56 p.m.