Triple
T22360183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Corel Corporation |
E552758
|
entity |
| Predicate | product |
P490
|
FINISHED |
| Object | WordPerfect |
—
|
NE NERFINISHED |
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: WordPerfect | Statement: [Corel Corporation, product, WordPerfect]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WordPerfect Context triple: [Corel Corporation, product, WordPerfect]
-
A.
Corel WordPerfect Office
chosen
Corel WordPerfect Office is an office productivity suite by Corel that includes word processing, spreadsheet, presentation, and related tools, historically popular as an alternative to Microsoft Office.
-
B.
WordStar
WordStar is a pioneering word processing software program that was widely used on early personal computers in the late 1970s and 1980s.
-
C.
Lotus Word Pro
Lotus Word Pro is a word processing application developed by Lotus as part of the Lotus SmartSuite office productivity package.
-
D.
WordPad
WordPad is a basic word processing application for Microsoft Windows that offers more features than Notepad but fewer than full office suites like Microsoft Word.
-
E.
Word
Word is Microsoft’s widely used word processing application for creating, editing, and formatting text documents.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e4affcc8190ba7c27d29062558d |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f157d3852c819082518851568e1b51 |
completed | April 29, 2026, 12:58 a.m. |
Created at: April 16, 2026, 8:44 p.m.