Triple
T505125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Italian Banditti |
E10486
|
entity |
| Predicate | hasFictionalElement |
P795
|
FINISHED |
| Object | Italian outlaws |
—
|
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: Italian outlaws | Statement: [The Italian Banditti, hasFictionalElement, Italian outlaws]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalElement Context triple: [The Italian Banditti, hasFictionalElement, Italian outlaws]
-
A.
hasNotableFictionalBearer
Indicates that an entity is associated with at least one well-known fictional character that bears its name or designation.
-
B.
usesElement
Indicates that one entity makes use of, incorporates, or depends on a specified element in its structure, function, or behavior.
-
C.
hasMediaFranchise
Indicates that one entity is part of, or belongs to, a larger media franchise represented by another entity.
-
D.
hasMetafictionalRole
Indicates that an entity plays a role within a story that self-consciously comments on, references, or breaks the conventions of fiction itself.
-
E.
appearsIn
chosen
Indicates that an entity is present, featured, or occurs within a particular context, work, or medium.
- 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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f14b2acc8190818e8a53eac69c54 |
completed | Feb. 28, 2026, 1:44 p.m. |
| PD | Predicate disambiguation | batch_69a2edfce7a08190a408bc019de60d5d |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.