Triple
T26032516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nd Cube |
E647471
|
entity |
| Predicate | hasNotableFranchisePartner |
P188865
|
FINISHED |
| Object | Mario |
—
|
NE NERFINISHED |
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: Mario | Statement: [Nd Cube, hasNotableFranchisePartner, Mario]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableFranchisePartner Context triple: [Nd Cube, hasNotableFranchisePartner, Mario]
-
A.
hasNotableFranchise
Indicates that an entity is associated with a well-known, significant, or widely recognized franchise.
-
B.
hasNetworkPartner
Indicates that an entity is connected to another entity through a formal or recognized network partnership relationship.
-
C.
hasFranchiseConnection
Indicates a relationship where two entities are linked through a franchise arrangement, such as licensing, branding, or operational affiliation within the same franchise system.
-
D.
participatingFranchise
Indicates that a franchise is involved as a participant in a particular program, event, or arrangement.
-
E.
associatedFranchise
Indicates a relationship where one entity is linked to, or belongs within, a particular franchise or franchise universe.
- F. None of above. chosen
Provenance (4 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_69e77e8b60e88190a3b26c4f0032a2c2 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69fbaf18085481908c774e8f8bbb9a41 |
completed | May 6, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69fbadf1e6008190a71bbd196ba06844 |
completed | May 6, 2026, 9:09 p.m. |
| PDg | Predicate description generation | batch_69fbaebbb7f88190b4edfd9b83550aad |
completed | May 6, 2026, 9:12 p.m. |
Created at: April 22, 2026, 9:06 a.m.