Triple
T7137779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Ears |
E166351
|
entity |
| Predicate | setting |
P1957
|
FINISHED |
| Object | Toyland |
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 | Statement: [Big Ears, setting, Toyland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toyland Context triple: [Big Ears, setting, Toyland]
-
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.
Kiddyland
Kiddyland is a children’s amusement area within Playland Park featuring kid-friendly rides and attractions.
-
C.
Adventureland
Adventureland is a themed land found in several Disney parks, designed to evoke exotic, tropical locales through attractions, lush landscaping, and immersive storytelling.
-
D.
Adventureland
Adventureland is a 2009 coming-of-age comedy-drama film set in a 1980s amusement park, known for its blend of humor and bittersweet romance.
-
E.
Fantasyland
Fantasyland is a themed area in Disney parks that brings classic fairy tales and animated stories to life through rides, attractions, and immersive environments.
- 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.