Triple
T1647937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VBA |
E35623
|
entity |
| Predicate | runsInside |
P23495
|
FINISHED |
| Object | Microsoft PowerPoint |
E34629
|
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: Microsoft PowerPoint | Statement: [VBA, runsInside, Microsoft PowerPoint]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Microsoft PowerPoint Context triple: [VBA, runsInside, Microsoft PowerPoint]
-
A.
PowerPoint
chosen
PowerPoint is a widely used Microsoft presentation software application for creating, editing, and delivering slide-based visual presentations.
-
B.
LibreOffice Impress
LibreOffice Impress is the free, open-source presentation component of the LibreOffice suite, used to create and display slide-based presentations across multiple platforms.
-
C.
Google Slides
Google Slides is a web-based presentation application by Google that lets users create, edit, collaborate on, and present slide decks online.
-
D.
Prezi
Prezi is a cloud-based presentation software known for its zoomable, non-linear canvas that offers a more dynamic alternative to traditional slide-based tools.
-
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_69a8860568888190a32cd9f70acbba42 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa61e142ac8190aa2fbd8f0826b5b2 |
completed | March 6, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad60a73a288190a659e2a1f09ba524 |
completed | March 8, 2026, 11:42 a.m. |
Created at: March 4, 2026, 7:29 p.m.