Triple
T4322788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Conner4Real |
E96556
|
entity |
| Predicate | usesFictionalTechnology |
P50195
|
FINISHED |
| Object | Aquila visual album helmet |
—
|
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: Aquila visual album helmet | Statement: [Conner4Real, usesFictionalTechnology, Aquila visual album helmet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesFictionalTechnology Context triple: [Conner4Real, usesFictionalTechnology, Aquila visual album helmet]
-
A.
hasFictionalForm
Indicates that an entity has a counterpart or representation that exists within a fictional or imaginary context.
-
B.
hasFictionalUniverseElement
Indicates that one entity is a component, feature, or constituent part of the fictional universe represented by the other entity.
-
C.
fictionalUse
chosen
Indicates that one entity makes use of another within a fictional or imaginary context, rather than in real-world usage.
-
D.
technologyUsedIn
Indicates that a particular technology is employed or applied within a specific process, product, context, or domain.
-
E.
usesEquipment
Indicates that an entity employs or operates a particular piece of equipment to perform an action or fulfill a function.
- 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_69b345422aac81909ddbadae437d122e |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b351177eb88190b89fa49a88add5e8 |
completed | March 12, 2026, 11:49 p.m. |
| PD | Predicate disambiguation | batch_69b34f4bec888190987fc2631498b637 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:12 p.m.