Triple
T32677700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black Gold |
E835473
|
entity |
| Predicate | featuresChildrenInVideo |
P198622
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Black Gold, featuresChildrenInVideo, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresChildrenInVideo Context triple: [Black Gold, featuresChildrenInVideo, yes]
-
A.
featuresModelInVideo
Indicates that a video includes or showcases a particular model as part of its visual content.
-
B.
videoFeatures
Indicates that one entity possesses or includes specific characteristics, attributes, or elements of a video.
-
C.
featuresChildCharacters
chosen
Indicates that the subject includes or prominently presents child characters as part of its content or narrative.
-
D.
musicVideoCharacterName
Indicates that the predicate specifies the name of a character appearing in a music video.
-
E.
featuresResponseOfPeople
Indicates a relationship where something presents or includes the reactions, opinions, or feedback expressed by people.
- 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_69f3493134b48190aa3c8cb523bd3800 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a01302a80ec8190a27692f09ac38a80 |
completed | May 11, 2026, 1:26 a.m. |
| PD | Predicate disambiguation | batch_6a012fad62308190a53f8b4a071eb245 |
completed | May 11, 2026, 1:23 a.m. |
Created at: May 1, 2026, 1:09 a.m.