Triple
T21430086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | J-Roc |
E528659
|
entity |
| Predicate | hasProfessionInUniverse |
P129585
|
FINISHED |
| Object | small-time criminal |
—
|
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: small-time criminal | Statement: [J-Roc, hasProfessionInUniverse, small-time criminal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionInUniverse Context triple: [J-Roc, hasProfessionInUniverse, small-time criminal]
-
A.
hasInUniverseRole
Indicates that an entity holds or performs a specific role or function within a particular fictional or defined universe.
-
B.
hasFictionalProfessionLevel
Indicates that an entity holds a fictional or imagined profession at a specified level, rank, or degree of expertise.
-
C.
hasProfessionTrait
Indicates that an entity possesses a particular characteristic, quality, or attribute specifically related to their profession or occupational role.
-
D.
hasGivenProfession
Indicates that an entity holds or practices a specified profession or occupation.
-
E.
representsProfessionIn
chosen
Indicates that an entity holds or is associated with a particular profession within a specified context, domain, or location.
- 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_69ee813ef6a8819089511b8f608c9491 |
completed | April 26, 2026, 9:18 p.m. |
| PD | Predicate disambiguation | batch_69e61639ee288190889ffd500d1260f6 |
completed | April 20, 2026, 12:04 p.m. |
Created at: April 16, 2026, 5:49 p.m.