Triple
T7802369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beauty and the Beast (musical) |
E180460
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Beast |
E668463
|
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: Beast | Statement: [Beauty and the Beast (musical), featuresCharacter, Beast]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beast Context triple: [Beauty and the Beast (musical), featuresCharacter, Beast]
-
A.
Beast
Beast is a brilliant mutant scientist and acrobatic fighter known for his blue-furred, beast-like appearance and long-standing membership in the X-Men and Avengers.
-
B.
Beast
Beast is the short name of the Brampton Beast, a former professional ice hockey team based in Brampton, Ontario, that competed in the ECHL.
-
C.
Beast
Beast is a thriller novel by Peter Benchley that centers on a deadly giant squid terrorizing a coastal community.
-
D.
Beast
chosen
Beast is the cursed prince who transforms into a monstrous creature and must learn love and compassion to break the spell in Disney’s 2017 live-action adaptation of "Beauty and the Beast."
-
E.
The Beast
The Beast was the fearsome nickname of Jimmie Foxx, a legendary power-hitting Major League Baseball slugger of the early 20th century.
- 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_69ca827e50cc8190a92a733577184938 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cae988bc2081909870bae1c2e9c238 |
completed | March 30, 2026, 9:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb14371e2081908d0a798d3b785c3c |
completed | March 31, 2026, 12:24 a.m. |
Created at: March 30, 2026, 4:33 p.m.