Triple
T4073323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | "What can Brown do for you?" |
E86699
|
entity |
| Predicate | associatedWithBrandColor |
P27889
|
FINISHED |
| Object | brown |
—
|
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: brown | Statement: ["What can Brown do for you?", associatedWithBrandColor, brown]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithBrandColor Context triple: ["What can Brown do for you?", associatedWithBrandColor, brown]
-
A.
associatedBrandCategory
Indicates that a brand is linked to or classified under a particular product or service category.
-
B.
associatedWithBrandValues
Indicates that one entity is connected or aligned with the brand values, principles, or identity represented by another entity.
-
C.
usedBrand
Indicates that an entity has utilized, applied, or operated a particular brand in some context.
-
D.
hasBranding
Indicates that one entity carries, displays, or is associated with the brand identity of another entity.
-
E.
corporateColor
chosen
Indicates the official color or color scheme that represents a corporation’s brand or identity.
- 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_69aed93ebe448190a1f1686e28740ac9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefc22be988190a2b6575d4f5e0f7b |
completed | March 9, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69aef9061d2481908307cafc9e9b32c0 |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:39 p.m.