Triple
T3329973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tangled |
E70009
|
entity |
| Predicate | basedOn |
P98
|
FINISHED |
| Object | Rapunzel |
E126644
|
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: Rapunzel | Statement: [Tangled, basedOn, Rapunzel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rapunzel Context triple: [Tangled, basedOn, Rapunzel]
-
A.
Rapunzel
chosen
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.
-
B.
Elsa
Elsa is a feminine given name of Germanic origin, widely recognized today through its use for the main character in Disney's animated film "Frozen."
-
C.
Tangled
Tangled is a 2010 Disney animated musical fantasy film that reimagines the Rapunzel fairy tale with a blend of comedy, adventure, and computer-generated animation.
-
D.
Sofia the First
Sofia the First is an animated Disney Junior television series that follows a young girl who becomes a princess and learns life lessons in a magical kingdom.
-
E.
Tinker Bell
Tinker Bell is a mischievous fairy from the Peter Pan stories, best known for her small size, magical pixie dust, and iconic status in popular culture and Disney media.
- 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_69ad85a24f208190bcf83131bfed3521 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb171ee0881908642504ab0ac8329 |
completed | March 8, 2026, 5:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b31a810e2c8190bfc206bdeb1ac5b8 |
completed | March 12, 2026, 7:56 p.m. |
Created at: March 8, 2026, 3:12 p.m.