Triple
T16612033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosie |
E403597
|
entity |
| Predicate | hasFictionalUniverseCharacteristic |
P84656
|
FINISHED |
| Object | show business setting |
—
|
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: show business setting | Statement: [Rosie, hasFictionalUniverseCharacteristic, show business setting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalUniverseCharacteristic Context triple: [Rosie, hasFictionalUniverseCharacteristic, show business setting]
-
A.
hasFictionalUniverseElement
Indicates that one entity is a component, feature, or constituent part of the fictional universe represented by the other entity.
-
B.
hasFictionalUniverseProperty
chosen
Indicates that a fictional universe possesses a specific characteristic, attribute, or property.
-
C.
hasFictionalUniverseType
Indicates that an entity is associated with, or belongs to, a particular type or category of fictional universe.
-
D.
hasFictionalUniverseGenre
Indicates that a fictional universe is associated with a particular genre that characterizes its overall style, themes, or narrative type.
-
E.
hasFictionalScope
Indicates that something pertains to, applies within, or is limited to a fictional or imagined context rather than real-world scope.
- 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_69d883880d0c81908b5fcd454e767b60 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e36096356c819092815d64db041793 |
completed | April 18, 2026, 10:44 a.m. |
| PD | Predicate disambiguation | batch_69e296aabc508190b3836a91b49113ad |
completed | April 17, 2026, 8:23 p.m. |
Created at: April 10, 2026, 5:17 a.m.