Triple
T35282405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brown Skin |
E1018967
|
entity |
| Predicate | hasTargetOfAffection |
P140057
|
FINISHED |
| Object | a dark-skinned lover |
—
|
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: a dark-skinned lover | Statement: [Brown Skin, hasTargetOfAffection, a dark-skinned lover]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTargetOfAffection Context triple: [Brown Skin, hasTargetOfAffection, a dark-skinned lover]
-
A.
objectOfAffectionFor
chosen
Indicates that one entity is the target or recipient of another entity’s romantic or affectionate feelings.
-
B.
aimedAtBy
Indicates that one entity serves as the target or goal toward which another entity directs an action, intention, or focus.
-
C.
hasAffectionateUse
Indicates that one entity uses or refers to another in a loving, tender, or emotionally warm manner.
-
D.
hasFetish
Indicates a relationship where one entity has a strong sexual fixation, attraction, or arousal specifically focused on another entity or on a particular object, body part, or activity.
-
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_69f76de6d39c8190bb11342e4b91ff2b |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f7b0e5744c8190a22c1e1d6fcfa466 |
completed | May 3, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f7ab70d034819080295628497d8582 |
completed | May 3, 2026, 8:09 p.m. |
Created at: May 3, 2026, 4:03 p.m.