Triple
T29070064
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Llame Pa' Verte (Bailando Sexy) |
E735788
|
entity |
| Predicate | hasTargetSetting |
P3538
|
FINISHED |
| Object | nightclubs |
—
|
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: nightclubs | Statement: [Llame Pa' Verte (Bailando Sexy), hasTargetSetting, nightclubs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTargetSetting Context triple: [Llame Pa' Verte (Bailando Sexy), hasTargetSetting, nightclubs]
-
A.
hasSetting
chosen
Indicates that an entity takes place, occurs, or exists within a particular environment, context, or location.
-
B.
hasSettingBy
Indicates that something (such as a work, event, or scenario) has its contextual environment, location, or background defined or established by a particular agent or source.
-
C.
hasTarget
Indicates that one entity is directed toward, aimed at, or intended to affect another specific entity as its target.
-
D.
hasSettingRole
Indicates that an entity participates in a setting by fulfilling a specific contextual or functional role within it.
-
E.
hasUserSetting
Indicates that a user is associated with a specific configuration or preference setting.
- 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_69f077e9b0a48190bb79548279cb7f64 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69f68f670b608190a0b6ab60d722b4e0 |
completed | May 2, 2026, 11:57 p.m. |
| PD | Predicate disambiguation | batch_69f68b7b03488190b1db5fde4c7dd6e5 |
completed | May 2, 2026, 11:40 p.m. |
Created at: April 28, 2026, 10:19 a.m.