Triple
T1518573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mickey Mouse franchise |
E32173
|
entity |
| Predicate | hasMerchandiseCategory |
P7224
|
FINISHED |
| Object | toys |
—
|
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: toys | Statement: [Mickey Mouse franchise, hasMerchandiseCategory, toys]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMerchandiseCategory Context triple: [Mickey Mouse franchise, hasMerchandiseCategory, toys]
-
A.
hasCategoryOn
Indicates that something is assigned to or associated with a specific category within a given context or scope.
-
B.
hasMarketingCategory
chosen
Indicates that an entity is associated with a specific marketing category or segment used for classification or targeting.
-
C.
hasRetailCategory
Indicates that an entity is associated with a specific retail category or type of retail business.
-
D.
hasRelatedCategory
Indicates that one category is associated with another category through a non-hierarchical, contextually relevant relationship.
-
E.
hasCategoryGroup
Indicates that something is associated with, or belongs to, a broader grouping of related categories.
- 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_69a885e8caf88190a5fbb6159ce87786 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a93d4756888190bf3872154de11539 |
completed | March 5, 2026, 8:22 a.m. |
| PD | Predicate disambiguation | batch_69a907ac7ea081908dd95bb5cc3b9847 |
completed | March 5, 2026, 4:33 a.m. |
Created at: March 4, 2026, 7:26 p.m.