Triple
T7394119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Symantec (historical headquarters) |
E170577
|
entity |
| Predicate | industryOfOccupant |
P62538
|
FINISHED |
| Object | cybersecurity |
—
|
LITERAL 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: cybersecurity | Statement: [Symantec (historical headquarters), industryOfOccupant, cybersecurity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: industryOfOccupant Context triple: [Symantec (historical headquarters), industryOfOccupant, cybersecurity]
-
A.
occupationType
Indicates the specific kind or category of work, profession, or role that an entity performs or holds.
-
B.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
-
C.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
-
D.
ownerOccupation
Indicates that the occupation or job role of an entity that owns something is being specified or described.
-
E.
hasOccupationSector
chosen
Indicates that an entity’s occupation belongs to or is categorized within a particular economic or professional sector.
- F. None of above.
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_69c68a5f04188190ac266569c9280347 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f2263b48819089319a2a2f0d3357 |
completed | March 27, 2026, 9:09 p.m. |
| PD | Predicate disambiguation | batch_69c6f0309cc88190b55d278969400294 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:09 p.m.