Triple
T22783345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norton Antivirus |
E563896
|
entity |
| Predicate | developer |
P73
|
FINISHED |
| Object | Symantec Corporation |
—
|
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: Symantec Corporation | Statement: [Norton Antivirus, developer, Symantec Corporation]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Symantec Corporation Context triple: [Norton Antivirus, developer, Symantec Corporation]
-
A.
Symantec
chosen
Symantec is a cybersecurity and software company best known for its Norton antivirus products and enterprise security solutions.
-
B.
Trend Micro
Trend Micro is a global cybersecurity company known for its antivirus, cloud security, and enterprise threat protection solutions.
-
C.
McAfee
McAfee is a global cybersecurity company best known for its antivirus and digital security software for consumers and businesses.
-
D.
Veritas Software
Veritas Software was a prominent enterprise data management and storage software company known for its backup, recovery, and availability solutions before being acquired by Symantec.
-
E.
AVG Technologies
AVG Technologies is a cybersecurity company best known for its antivirus and internet security software for consumers and small businesses.
- 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_69e2455500788190b4b33030461f3bbd |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17c2ee4e88190951afb2abe69009f |
completed | April 29, 2026, 3:34 a.m. |
Created at: April 17, 2026, 3:29 p.m.