Triple
T5751971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken |
E126873
|
entity |
| Predicate | setting |
P1957
|
FINISHED |
| Object | Barbieland |
E126875
|
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: Barbieland | Statement: [Ken, setting, Barbieland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barbieland Context triple: [Ken, setting, Barbieland]
-
A.
Barbieland
chosen
Barbieland is a vibrant, hyper-stylized fantasy world where various Barbies and Kens live in an idealized matriarchal society.
-
B.
Barbie
Barbie is a 2023 fantasy-comedy film directed by Greta Gerwig that reimagines the iconic Mattel doll in a satirical, self-aware story exploring gender roles, identity, and consumer culture.
-
C.
Kiddyland
Kiddyland is a children’s amusement area within Playland Park featuring kid-friendly rides and attractions.
-
D.
Toyland
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.
-
E.
Pluto Island
Pluto Island is one of the small islands within India’s Mahatma Gandhi Marine National Park in the Andaman and Nicobar Islands, known for its protected marine and coastal ecosystems.
- 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_69c00832aedc81909899801b141fa3b4 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0288b580c81909e1289982b106695 |
completed | March 22, 2026, 5:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07e3a50b88190a943b2d91d3c5b8e |
completed | March 22, 2026, 11:41 p.m. |
Created at: March 22, 2026, 3:48 p.m.