Triple
T15371987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Avast Software s.r.o. |
E367569
|
entity |
| Predicate | acquisitionTarget |
P50794
|
FINISHED |
| Object | AVG Technologies |
E1152461
|
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: AVG Technologies | Statement: [Avast Software s.r.o., acquisitionTarget, AVG Technologies]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AVG Technologies Context triple: [Avast Software s.r.o., acquisitionTarget, AVG Technologies]
-
A.
AVG Technologies
chosen
AVG Technologies is a cybersecurity company best known for its antivirus and internet security software for consumers and small businesses.
-
B.
McAfee
McAfee is a global cybersecurity company best known for its antivirus and digital security software for consumers and businesses.
-
C.
Kaspersky Lab
Kaspersky Lab is a Russian cybersecurity and anti-virus company known for developing security software and threat intelligence solutions used worldwide.
-
D.
Trend Micro
Trend Micro is a global cybersecurity company known for its antivirus, cloud security, and enterprise threat protection solutions.
-
E.
Symantec
Symantec is a cybersecurity and software company best known for its Norton antivirus products and enterprise security solutions.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e5c1d548190930bfaf0861595ae |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff13457418819088232270b092c969 |
completed | May 9, 2026, 10:58 a.m. |
Created at: April 10, 2026, 3:18 a.m.