Triple
T38114618
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | J’adore |
E951755
|
entity |
| Predicate | brandTagline |
P190570
|
FINISHED |
| Object | J’adore Dior |
—
|
NE NERFINISHED |
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: J’adore Dior | Statement: [J’adore, brandTagline, J’adore Dior]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: brandTagline Context triple: [J’adore, brandTagline, J’adore Dior]
-
A.
brandLine
Indicates that one brand is a specific product line or sub-brand offered under another brand.
-
B.
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.
-
C.
brandNumber
Indicates the identifying number or code assigned to a particular brand within a system or dataset.
-
D.
brandNameType
Indicates the specific type or category associated with a brand name within a broader branding or naming system.
-
E.
brandPosition
Indicates the relative market or perceptual placement of a brand compared to competitors or within a defined category.
- 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_69f76f07734c8190814e937e12257a78 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fccbd826708190b5fab12c4236299a |
completed | May 7, 2026, 5:28 p.m. |
| PD | Predicate disambiguation | batch_69fcc58838e08190b8fa54aa5c165f2d |
completed | May 7, 2026, 5:02 p.m. |
| PDg | Predicate description generation | batch_69fccbd6b7688190b746803cf78d5704 |
completed | May 7, 2026, 5:28 p.m. |
Created at: May 3, 2026, 4:21 p.m.