Triple
T18854822
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jarka Ruus |
E461142
|
entity |
| Predicate | featuresRealm |
P76995
|
FINISHED |
| Object | Forbidding |
—
|
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: Forbidding | Statement: [Jarka Ruus, featuresRealm, Forbidding]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresRealm Context triple: [Jarka Ruus, featuresRealm, Forbidding]
-
A.
featuresReturnOf
Indicates that something (such as a work, event, or product) includes or highlights the comeback or reappearance of a person, character, element, or feature.
-
B.
featuresIn
Indicates that an entity appears or plays a role within another entity, such as a person or element being included in a work, event, or context.
-
C.
featuresCharacterWith
Indicates that one entity (such as a work or product) includes or presents a particular character as part of its content.
-
D.
featuresFictionalElement
Indicates that one entity includes, presents, or incorporates a fictional element (such as an imaginary character, place, object, or concept) as part of its content or composition.
-
E.
featuresUniverse
chosen
Indicates that one entity includes, presents, or prominently showcases a particular universe or world as part of its content or structure.
- 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_69d8dcfb7b9c8190854e7b171b98ea2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c05c16e88190a08b6a8b94e1c9f9 |
completed | April 20, 2026, 5:57 a.m. |
| PD | Predicate disambiguation | batch_69e48d2166b88190add38de96cedc65c |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:57 a.m.