Triple
T29823937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liquid Lips |
E757330
|
entity |
| Predicate | containsSexuallyExplicitContent |
P47549
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Liquid Lips, containsSexuallyExplicitContent, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsSexuallyExplicitContent Context triple: [Liquid Lips, containsSexuallyExplicitContent, true]
-
A.
containsAdultContent
chosen
Indicates that the referenced item includes material intended for adults, such as explicit sexual, violent, or otherwise age-restricted content.
-
B.
depictsSex
Indicates that one entity visually represents or portrays sexual activity or sexual content involving another entity.
-
C.
hasSexualityCharacteristic
Indicates that an entity possesses a specific sexual orientation or sexuality-related characteristic.
-
D.
containsPersonalContent
Indicates that the referenced item includes content that is personal in nature, such as private details, sensitive information, or individually identifying material.
-
E.
isErotic
Indicates that something involves or expresses sexual desire, arousal, or sensuality.
- 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_69f22457c84c8190a6d9f56bc74082a9 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69fec5c503808190bde0b1829ea43071 |
completed | May 9, 2026, 5:27 a.m. |
| PD | Predicate disambiguation | batch_69fec535dd6c8190a6ab85201f5a04a9 |
completed | May 9, 2026, 5:25 a.m. |
Created at: April 29, 2026, 5:30 p.m.