Triple
T7137940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dinah Doll |
E166355
|
entity |
| Predicate | setting |
P1957
|
FINISHED |
| Object | Toyland market |
E166348
|
NE 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: Toyland market | Statement: [Dinah Doll, setting, Toyland market]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toyland market Context triple: [Dinah Doll, setting, Toyland market]
-
A.
Toyland
chosen
Toyland is the colorful, whimsical fantasy world that serves as the primary setting for Enid Blyton’s Noddy stories, inhabited by living toys and playful characters.
-
B.
Storybook Land
Storybook Land is a fairy tale–themed amusement park in Aberdeen, South Dakota, featuring storybook characters, themed rides, and attractions for young children and families.
-
C.
Al’s Toy Barn
Al’s Toy Barn is the fictional toy store owned by the greedy collector Al McWhiggin in Pixar’s animated film Toy Story 2.
-
D.
Star Market
Star Market is a regional supermarket chain in New England offering groceries, fresh produce, and household goods.
-
E.
Downtown Disney
Downtown Disney is an outdoor shopping, dining, and entertainment district located adjacent to the Disneyland Resort in Anaheim, California.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c68884a9388190af42f90d1c1a7151 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e6939b788190929e92ff481f2ee4 |
completed | March 27, 2026, 8:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7a34b99048190a8e77cd0fe253611 |
completed | March 28, 2026, 9:45 a.m. |
Created at: March 27, 2026, 2:45 p.m.