Triple
T7742110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dopey |
E175534
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Snow White |
E632975
|
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: Snow White | Statement: [Dopey, associatedWith, Snow White]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snow White Context triple: [Dopey, associatedWith, Snow White]
-
A.
Snow White
chosen
Snow White is the kind-hearted princess and central heroine of Disney’s classic 1937 animated film "Snow White and the Seven Dwarfs," renowned as the studio’s first feature-length animated character.
-
B.
Princess Aurora
Princess Aurora is the kind-hearted, golden-haired princess from Disney’s Sleeping Beauty, known for her enchanted slumber and her central role in the reimagined Maleficent films.
-
C.
Snowy White
Snowy White is a British blues and rock guitarist known for his work with Thin Lizzy, Pink Floyd-related projects, and his own solo career.
-
D.
Cinderella
Cinderella is a musical by Andrew Lloyd Webber that reimagines the classic fairy tale with a modern twist in story, character, and score.
-
E.
Rapunzel
Rapunzel is a classic fairy-tale princess best known for her extraordinarily long hair and her story of captivity in a tower and eventual escape.
- 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_69c6995f9c60819092e386192bd63c6f |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c70387807081909546bc7c209955ef |
completed | March 27, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c93fa2550481908348b8b4dd23d6df |
completed | March 29, 2026, 3:05 p.m. |
Created at: March 27, 2026, 4:07 p.m.