Triple
T2095278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cupertino |
E32765
|
entity |
| Predicate | majorEmployer |
P588
|
FINISHED |
| Object |
Trend Micro
Trend Micro is a global cybersecurity company known for its antivirus, cloud security, and enterprise threat protection solutions.
|
E234894
|
NE FINISHED |
How this triple was built (4 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: Trend Micro | Statement: [Cupertino, majorEmployer, Trend Micro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trend Micro Context triple: [Cupertino, majorEmployer, Trend Micro]
-
A.
Symantec
Symantec is a cybersecurity and software company best known for its Norton antivirus products and enterprise security solutions.
-
B.
Micro Focus
Micro Focus is a British multinational software and information technology company known for providing enterprise-level application modernization, testing, and management solutions.
-
C.
Duo Security
Duo Security is a cybersecurity company best known for its cloud-based multi-factor authentication and zero-trust access solutions.
-
D.
XProtect
XProtect is a video management software platform widely used for managing and recording IP-based surveillance systems.
-
E.
Novell
Novell was a prominent software company best known for its NetWare network operating system and contributions to enterprise networking and Linux technologies.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Trend Micro Triple: [Cupertino, majorEmployer, Trend Micro]
Generated description
Trend Micro is a global cybersecurity company known for its antivirus, cloud security, and enterprise threat protection solutions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Trend Micro Target entity description: Trend Micro is a global cybersecurity company known for its antivirus, cloud security, and enterprise threat protection solutions.
-
A.
Symantec
Symantec is a cybersecurity and software company best known for its Norton antivirus products and enterprise security solutions.
-
B.
Micro Focus
Micro Focus is a British multinational software and information technology company known for providing enterprise-level application modernization, testing, and management solutions.
-
C.
Duo Security
Duo Security is a cybersecurity company best known for its cloud-based multi-factor authentication and zero-trust access solutions.
-
D.
XProtect
XProtect is a video management software platform widely used for managing and recording IP-based surveillance systems.
-
E.
Novell
Novell was a prominent software company best known for its NetWare network operating system and contributions to enterprise networking and Linux technologies.
- F. None of above. chosen
Provenance (5 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_69a885eba0708190999696a45cbec816 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abba99ddc48190bb2097b56efb7aca |
completed | March 7, 2026, 5:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae305cb77c819085c4f3eb2223f749 |
completed | March 9, 2026, 2:28 a.m. |
| NEDg | Description generation | batch_69ae30f6b7c4819080cb7cb7adc1f6d3 |
completed | March 9, 2026, 2:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae31871d408190a4ae64372660fa79 |
completed | March 9, 2026, 2:33 a.m. |
Created at: March 4, 2026, 7:43 p.m.