Triple
T20712604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VMW |
E509084
|
entity |
| Predicate | tickerFor |
P9230
|
FINISHED |
| Object | VMware, Inc. |
—
|
NE NERFINISHED |
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: VMware, Inc. | Statement: [VMW, tickerFor, VMware, Inc.]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: VMware, Inc. Context triple: [VMW, tickerFor, VMware, Inc.]
-
A.
VMware
chosen
VMware is a leading American cloud computing and virtualization technology company known for its pioneering hypervisor and software-defined data center solutions.
-
B.
Citrix Systems
Citrix Systems is an American software company best known for its virtualization, remote access, and cloud computing technologies that enable secure delivery of applications and desktops.
-
C.
Nutanix
Nutanix is a cloud computing company best known for pioneering hyper-converged infrastructure software that simplifies data center and multicloud operations.
-
D.
Azul Systems
Azul Systems is a software company specializing in high-performance, scalable Java runtimes and JVM technologies for enterprise applications.
-
E.
Hewlett Packard Enterprise
Hewlett Packard Enterprise is a major American multinational enterprise IT company that provides servers, storage, networking, and related services to business and government customers worldwide.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4c40ad88190b81f77695366d328 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c1ce84e48190922db93ed4b5d21b |
completed | April 21, 2026, 12:16 a.m. |
Created at: April 16, 2026, 12:15 p.m.