Triple
T21432259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blanket (Bigi) Jackson |
E528715
|
entity |
| Predicate | interests |
P66206
|
FINISHED |
| Object | film and media |
—
|
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: film and media | Statement: [Blanket (Bigi) Jackson, interests, film and media]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: interests Context triple: [Blanket (Bigi) Jackson, interests, film and media]
-
A.
subjectInterest
chosen
Indicates that the subject has an interest in, or is concerned with, the object.
-
B.
primaryInterest
Indicates that one entity is the main or most significant focus of attention, concern, or engagement for another entity.
-
C.
interestType
Indicates the specific category or nature of interest that one entity has in relation to another or to a subject.
-
D.
secondaryInterest
Indicates that an entity has a secondary, less primary but still relevant interest or focus in relation to another entity or topic.
-
E.
collectorInterest
Indicates that one entity has a special interest in acquiring, owning, or seeking out another entity as part of a collection.
- 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_69e0c455f3688190810bc96365791b0f |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b5326c8481908a6ab7d2dd961ae2 |
completed | April 22, 2026, 11:46 a.m. |
| PD | Predicate disambiguation | batch_69e61639ee288190889ffd500d1260f6 |
completed | April 20, 2026, 12:04 p.m. |
Created at: April 16, 2026, 5:50 p.m.