Triple
T38244243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EleVen by Venus Williams |
E1013850
|
entity |
| Predicate | hasBrandMessage |
P192194
|
FINISHED |
| Object | exceeding expectations |
—
|
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: exceeding expectations | Statement: [EleVen by Venus Williams, hasBrandMessage, exceeding expectations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBrandMessage Context triple: [EleVen by Venus Williams, hasBrandMessage, exceeding expectations]
-
A.
hasBrandName
Indicates that an entity is associated with or identified by a specific brand name.
-
B.
hasBrandType
Indicates that an entity is associated with or categorized under a particular brand type or classification.
-
C.
hasBrandForm
Indicates that one entity is a specific branded form or commercial variant of another entity.
-
D.
hasBrandConcept
Indicates that an entity is associated with or embodies a particular brand concept or branding idea.
-
E.
isBrand
Indicates that one entity functions as the commercial brand or label associated with another entity.
- 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_69f76dd7e89c8190b7866bc85aea521b |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fcf825ca7081909d06b0df33eb33f9 |
completed | May 7, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69fcf42160f0819096812a8bf590875e |
completed | May 7, 2026, 8:20 p.m. |
| PDg | Predicate description generation | batch_69fcf82405c88190a19cecf8e9cc272d |
completed | May 7, 2026, 8:37 p.m. |
Created at: May 3, 2026, 4:30 p.m.