Triple
T10006717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | McAfee-Maggart |
E198271
|
entity |
| Predicate | componentSurname |
P5298
|
FINISHED |
| Object | McAfee |
E618960
|
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: McAfee | Statement: [McAfee-Maggart, componentSurname, McAfee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: McAfee Context triple: [McAfee-Maggart, componentSurname, McAfee]
-
A.
McAfee
chosen
McAfee is a global cybersecurity company best known for its antivirus and digital security software for consumers and businesses.
-
B.
Symantec
Symantec is a cybersecurity and software company best known for its Norton antivirus products and enterprise security solutions.
-
C.
Trend Micro
Trend Micro is a global cybersecurity company known for its antivirus, cloud security, and enterprise threat protection solutions.
-
D.
Sophos
Sophos is a British cybersecurity company known for providing antivirus, endpoint protection, and network security solutions to businesses and organizations worldwide.
-
E.
Kaspersky Lab
Kaspersky Lab is a Russian cybersecurity and anti-virus company known for developing security software and threat intelligence solutions used worldwide.
- 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_69ca830fcca48190bbbd9b20c233835f |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd16afb481909a5d5893e024683f |
completed | April 2, 2026, 1:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d26a5d7e088190b5b1a852bfdc9073 |
completed | April 5, 2026, 1:57 p.m. |
Created at: March 30, 2026, 8:52 p.m.