Triple
T2948225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ryan Roslansky |
E79554
|
entity |
| Predicate | reportsTo |
P258
|
FINISHED |
| Object | Microsoft Corporation |
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 Corporation | Statement: [Ryan Roslansky, reportsTo, Microsoft Corporation]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Microsoft Corporation Context triple: [Ryan Roslansky, reportsTo, Microsoft Corporation]
-
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.
IBM
IBM is a multinational technology and consulting company known for its pioneering work in computer hardware, software, and enterprise services.
-
D.
Hewlett-Packard
Hewlett-Packard is a pioneering American technology company known for its innovations in computing, printers, and enterprise IT solutions.
-
E.
Apple Inc.
Apple Inc. is a multinational technology company best known for designing and selling consumer electronics like the iPhone, Mac, and iPad, along with software and digital 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_69ad8b1089588190b74d9e2505e45762 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad98b6cab88190b4dee7e5ec9347e5 |
completed | March 8, 2026, 3:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b0fc79f6d081908d3bbd52439f4d31 |
completed | March 11, 2026, 5:24 a.m. |
Created at: March 8, 2026, 2:57 p.m.