Triple
T12719586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oluwatosin Oluwole Ajibade |
E303938
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Skin Tight |
E985582
|
NE 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: Skin Tight | Statement: [Oluwatosin Oluwole Ajibade, notableWork, Skin Tight]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Skin Tight Context triple: [Oluwatosin Oluwole Ajibade, notableWork, Skin Tight]
-
A.
Skin Tight
chosen
"Skin Tight" is a popular Afrobeats song by Nigerian artist Mr Eazi that helped propel him to international recognition.
-
B.
Too Tight
"Too Tight" is a song by the Rolling Stones from their 1997 album "Bridges to Babylon," blending rock with contemporary production elements.
-
C.
Tighter & Tighter
Tighter & Tighter is a song by the American rock band Soundgarden from their 1996 album Down on the Upside.
-
D.
Tighten Up
"Tighten Up" is a Grammy-winning blues-rock song by American rock duo The Black Keys, known for its catchy whistle hook and prominent role in boosting the band's mainstream popularity.
-
E.
Skinned
Skinned is a song featured on the compilation album "Classic Masters."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d7bdf084148190ab9d513dc0735af4 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96411d87481909127e81755f23964 |
completed | April 10, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f67c8250108190bb7b3c93e590ea47 |
completed | May 2, 2026, 10:36 p.m. |
Created at: April 9, 2026, 5:24 p.m.