Triple
T9166881
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Bunny |
E219980
|
entity |
| Predicate | hairStyleCharacteristic |
P16252
|
FINISHED |
| Object | frequently changes hair color and style |
—
|
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: frequently changes hair color and style | Statement: [Bad Bunny, hairStyleCharacteristic, frequently changes hair color and style]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hairStyleCharacteristic Context triple: [Bad Bunny, hairStyleCharacteristic, frequently changes hair color and style]
-
A.
hairAsSymbol
Indicates that hair functions as a symbolic element representing ideas, traits, or meanings beyond its literal physical presence.
-
B.
hairDetail
chosen
Indicates a relationship that specifies particular characteristics or attributes of an entity’s hair, such as style, color, length, or texture.
-
C.
fashionCharacteristic
Indicates a relationship where one entity possesses or exhibits a particular style, trend, or fashion-related attribute in relation to another.
-
D.
tailpieceType
Indicates the specific kind or design of tailpiece associated with an instrument or object.
-
E.
hairCutOffBy
Indicates that one entity’s hair is removed or cut off by another entity.
- 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_69ca83e467108190abcae6a33b3d4dad |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccaade47cc81909b5c127dc8aa1340 |
completed | April 1, 2026, 5:19 a.m. |
| PD | Predicate disambiguation | batch_69cc6605c6808190a30d92da006206ac |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:22 p.m.