Triple
T9450786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melinda Ann French |
E227883
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Microsoft |
E1649
|
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 | Statement: [Melinda Ann French, employer, Microsoft]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Microsoft Context triple: [Melinda Ann French, employer, Microsoft]
-
A.
Microsoft
chosen
Microsoft is a multinational technology company best known for its Windows operating system, Office productivity suite, and Azure cloud computing platform.
-
B.
Micros Systems
Micros Systems was a leading provider of point-of-sale and hospitality management software and hardware solutions for restaurants, hotels, and retail businesses.
-
C.
WIN Corporation
WIN Corporation is an Australian media company best known for owning and operating the WIN Television network and related broadcasting assets.
-
D.
Microsoft Office
Microsoft Office is a widely used suite of productivity applications developed by Microsoft, including programs for word processing, spreadsheets, presentations, email, and more.
-
E.
IBM
IBM is a multinational technology and consulting company known for its pioneering work in computer hardware, software, and enterprise services.
- 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_69ca8439f8bc8190997f2ef40c9f0bc2 |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7f6649a48190b6844daa6202efe5 |
completed | April 1, 2026, 8:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12272a2dc8190892a22db0799b6a4 |
completed | April 4, 2026, 2:38 p.m. |
Created at: March 30, 2026, 7:51 p.m.