Triple
T26623554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serious Skincare |
E668272
|
entity |
| Predicate | hasTargetConcern |
P64698
|
FINISHED |
| Object | wrinkles |
—
|
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: wrinkles | Statement: [Serious Skincare, hasTargetConcern, wrinkles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTargetConcern Context triple: [Serious Skincare, hasTargetConcern, wrinkles]
-
A.
targetConcern
chosen
Indicates that something is the specific issue, problem, or subject that is the focus of attention, action, or consideration.
-
B.
hasTarget
Indicates that one entity is directed toward, aimed at, or intended to affect another specific entity as its target.
-
C.
hasThematicConcern
Indicates that one entity (such as a work, text, or discourse) centrally involves, addresses, or focuses on a particular theme, issue, or subject as a primary concern.
-
D.
hasTargetIssue
Indicates that an entity is associated with or directed toward a specific issue, problem, or concern as its focus.
-
E.
usesTarget
Indicates that one entity employs, applies, or operates on another entity as its target or object of action.
- 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_69ee9cff507c819092b95bf7219a702e |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f615e75cac8190972274bf5552a6d4 |
completed | May 2, 2026, 3:19 p.m. |
| PD | Predicate disambiguation | batch_69f60b8bb0d08190ab5a9a2a8847c6f4 |
completed | May 2, 2026, 2:34 p.m. |
Created at: April 27, 2026, 2:22 a.m.