Triple
T452076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Microsoft 365 |
E7151
|
entity |
| Predicate | includes |
P1393
|
FINISHED |
| Object | Excel |
E5278
|
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: Excel | Statement: [Microsoft 365, includes, Excel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Excel Context triple: [Microsoft 365, includes, Excel]
-
A.
Excel
chosen
Excel is a widely used spreadsheet software by Microsoft that enables data organization, analysis, visualization, and basic to advanced analytics through formulas, functions, and tools like PivotTables.
-
B.
Google Sheets
Google Sheets is a cloud-based spreadsheet application by Google that enables users to create, edit, and collaboratively work on spreadsheets in real time through a web browser or mobile app.
-
C.
Lotus 1-2-3
Lotus 1-2-3 is a pioneering spreadsheet software program for personal computers that became a dominant business application in the 1980s.
-
D.
VBA
VBA (Visual Basic for Applications) is a Microsoft event-driven programming language used primarily to automate tasks and extend functionality in Office applications like Excel and Access.
-
E.
Power Pivot
Power Pivot is an Excel data modeling and analysis add-in that enables users to create sophisticated data models, relationships, and DAX calculations for business intelligence reporting.
- 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_69a2e7e4676c81909ea0dbdecac0687c |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ef854f7481909dc2207faf0327ec |
completed | Feb. 28, 2026, 1:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a44802e858819081a0b5b98bb25bce |
completed | March 1, 2026, 2:06 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.