Triple
T8703873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Microsoft 365 Education |
E206597
|
entity |
| Predicate | includesService |
P1393
|
FINISHED |
| Object | Word |
E56704
|
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: Word | Statement: [Microsoft 365 Education, includesService, Word]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Word Context triple: [Microsoft 365 Education, includesService, Word]
-
A.
Word
chosen
Word is Microsoft’s widely used word processing application for creating, editing, and formatting text documents.
-
B.
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.
-
C.
WordStar
WordStar is a pioneering word processing software program that was widely used on early personal computers in the late 1970s and 1980s.
-
D.
WPS
WPS (Wi-Fi Protected Setup) is a wireless network security standard designed to simplify the process of connecting devices to a Wi-Fi network, but it is widely known for serious security vulnerabilities that make it susceptible to brute-force attacks.
-
E.
WPS
WPS was a top-tier professional women’s soccer league in the United States that operated from 2009 to 2012.
- 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_69ca835645e881908f00e3c8b51da81d |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc58fa0a208190a520e0e1f7faaea9 |
completed | March 31, 2026, 11:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cef41e3ec08190b29adf483cdf8cc3 |
completed | April 2, 2026, 10:56 p.m. |
Created at: March 30, 2026, 6:34 p.m.