Triple
T20349618
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wiseacre's Wizarding Equipment |
E495972
|
entity |
| Predicate | hasRealWorldCounterpartType |
P91337
|
FINISHED |
| Object | theme park shop |
—
|
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: theme park shop | Statement: [Wiseacre's Wizarding Equipment, hasRealWorldCounterpartType, theme park shop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRealWorldCounterpartType Context triple: [Wiseacre's Wizarding Equipment, hasRealWorldCounterpartType, theme park shop]
-
A.
hasRealWorldOrigin
Indicates that something is derived from, based on, or directly connected to an actual entity, event, or source in the real world.
-
B.
hasCounterpart
Indicates that one entity corresponds to, matches, or serves as an equivalent or parallel version of another entity.
-
C.
characterRealWorldCounterpart
Indicates that a fictional character is based on, inspired by, or directly corresponds to a specific real-world person.
-
D.
hasRealWorldVersion
chosen
Indicates that something has a corresponding or equivalent version that exists in the real, physical world.
-
E.
hasRealModel
Indicates that an abstract, theoretical, or simplified entity is associated with a corresponding concrete or physically instantiated model in the real world.
- 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_69e0b4a3320881909495ae8bc30bc2dc |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6784eacf4819095504e541d1d284d |
completed | April 20, 2026, 7:02 p.m. |
| PD | Predicate disambiguation | batch_69e57636b4808190bc2855af48a3ccdc |
completed | April 20, 2026, 12:41 a.m. |
Created at: April 16, 2026, 11:24 a.m.