Triple
T321519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sony VAIO computers |
E6423
|
entity |
| Predicate | hasBranding |
P11989
|
FINISHED |
| Object | VAIO logo |
—
|
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: VAIO logo | Statement: [Sony VAIO computers, hasBranding, VAIO logo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBranding Context triple: [Sony VAIO computers, hasBranding, VAIO logo]
-
A.
brandImage
Indicates the perceived overall impression, reputation, and associations that people hold about a particular brand.
-
B.
successorBranding
Indicates that one brand has replaced or continued another brand as its subsequent or updated identity.
-
C.
hasLogoText
Indicates that an entity’s logo includes specific textual content or wording.
-
D.
brandingScope
Indicates the extent or range within which a particular brand identity, strategy, or elements are applied or recognized.
-
E.
brand
Indicates that one entity is the commercial brand or label under which another entity (such as a product, service, or organization) is marketed or identified.
- F. None of above. chosen
Provenance (4 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_69a2e7933d6c8190bb2592ad13286ef2 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea81a1e88190b3496070eb3d85f5 |
completed | Feb. 28, 2026, 1:15 p.m. |
| PD | Predicate disambiguation | batch_69a2e948048c819098ba4de9261ef2ef |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea08878c8190a5e8a90f620a3888 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.