Triple
T28376344
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bing Bong’s Sweet Stuff |
E718765
|
entity |
| Predicate | hasThemeParkAreaType |
P167913
|
FINISHED |
| Object | land |
—
|
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: land | Statement: [Bing Bong’s Sweet Stuff, hasThemeParkAreaType, land]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasThemeParkAreaType Context triple: [Bing Bong’s Sweet Stuff, hasThemeParkAreaType, land]
-
A.
hasThemePark
Indicates that one entity owns, contains, or is associated with a theme park as part of its properties or offerings.
-
B.
hasThemeParkAreaIP
Indicates that a theme park area is based on, licensed from, or themed around a specific intellectual property (such as a franchise, brand, or media property).
-
C.
hasAttractionTheme
Indicates that something (such as a place, event, or attraction) is characterized by or associated with a particular theme or motif.
-
D.
locatedInThemeParkType
Indicates that an entity is situated within or belongs to a specific type or category of theme park.
-
E.
themeParkAttractionAt
Indicates that a specific theme park attraction is located at or associated with a particular theme park or site.
- 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_69eff6ee5afc8190bd7375a29f0cc6c6 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f66e5f7e30819094530abceabd5f43 |
completed | May 2, 2026, 9:36 p.m. |
| PD | Predicate disambiguation | batch_69f66abfdaf08190a55f14c70be6fd4d |
completed | May 2, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69f66d75a8788190aa9ca2c977429045 |
completed | May 2, 2026, 9:32 p.m. |
Created at: April 28, 2026, 1:03 a.m.