Triple
T37545397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bobbi Brown |
E933451
|
entity |
| Predicate | hasSkincareFocus |
P188246
|
FINISHED |
| Object | prepping skin for makeup |
—
|
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: prepping skin for makeup | Statement: [Bobbi Brown, hasSkincareFocus, prepping skin for makeup]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSkincareFocus Context triple: [Bobbi Brown, hasSkincareFocus, prepping skin for makeup]
-
A.
focusesOnSkinConcern
Indicates that something (such as a product, treatment, or content) is specifically directed toward addressing or improving a particular skin concern.
-
B.
facialMakeupIndicates
Indicates that the presence, style, or characteristics of facial makeup convey or signify a particular state, role, identity, or condition of an entity.
-
C.
cosmeticCategory
Indicates that one entity is classified as belonging to a particular cosmetic or beauty product category defined by the other entity.
-
D.
skinUse
Indicates that one entity uses, applies, or treats the skin of another entity in some manner.
-
E.
makeupType
Indicates the specific kind or category of makeup associated with an 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_69f76eca55bc8190acf25741793d5dac |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fba5eec0448190a5e6f0c43fdcd0e3 |
completed | May 6, 2026, 8:34 p.m. |
| PD | Predicate disambiguation | batch_69fba34edd548190bfa980e6e16e0a88 |
completed | May 6, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69fba5ee00fc81909be7b947a3f95034 |
completed | May 6, 2026, 8:34 p.m. |
Created at: May 3, 2026, 4:17 p.m.