Triple
T23410606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theophilus London |
E560056
|
entity |
| Predicate | fashionIndustryInvolvement |
P152154
|
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: [Theophilus London, fashionIndustryInvolvement, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fashionIndustryInvolvement Context triple: [Theophilus London, fashionIndustryInvolvement, yes]
-
A.
influencedFashionTrend
Indicates that one entity caused or contributed to a change or direction in another entity’s fashion style or prevailing clothing trends.
-
B.
fashionLabelSpecialty
Indicates that a fashion label is particularly focused on, known for, or specialized in a specific type of product, style, or design niche.
-
C.
fashionReputation
Indicates the perceived status or esteem an entity holds within the context of fashion, based on how its style, taste, or influence is judged by others.
-
D.
fashionBrandEndorsement
Indicates a relationship where a fashion brand formally supports, promotes, or is publicly associated with an entity (such as a person, product, or event) as an endorser.
-
E.
sponsorshipIndustry
Indicates a relationship where one entity sponsors another specifically within a given industry or sector context.
- F. None of above. chosen
Provenance (4 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_69e2454b3a5881909c64773dc8a5d289 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1a510b3848190ae42679ef0bcd424 |
completed | April 29, 2026, 6:28 a.m. |
| PD | Predicate disambiguation | batch_69f061ed34288190a2e5e8cae03b0095 |
completed | April 28, 2026, 7:29 a.m. |
| PDg | Predicate description generation | batch_69f07cbbd7488190ab3c8ae7d0fb68bf |
completed | April 28, 2026, 9:24 a.m. |
Created at: April 17, 2026, 5:38 p.m.