Triple
T7802392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beauty and the Beast (musical) |
E180460
|
entity |
| Predicate | tonyAwardWinner |
P16597
|
FINISHED |
| Object | Best Costume Design of a Musical |
—
|
LITERAL 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: Best Costume Design of a Musical | Statement: [Beauty and the Beast (musical), tonyAwardWinner, Best Costume Design of a Musical]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tonyAwardWinner Context triple: [Beauty and the Beast (musical), tonyAwardWinner, Best Costume Design of a Musical]
-
A.
tonyAwardsWonFor
chosen
Indicates the specific Tony Award-winning work or role for which an entity received a Tony Award.
-
B.
tonyAwardForBestActorInAPlayWork
Indicates that a work (play) is the performance or role for which an actor received the Tony Award for Best Actor in a Play.
-
C.
tonyNominations
Indicates that an entity has received one or more nominations for a Tony Award.
-
D.
tonyAwardsYear
Indicates the year in which a particular Tony Awards event or ceremony took place.
-
E.
OlivierAwardYear
Indicates the specific year in which an Olivier Award was given or associated with a particular recipient, work, or event.
- F. None of above.
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_69caf78a6d88819093f83528fe88b182 |
completed | March 30, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69cae9111b2481909684a2d4aa4831c2 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:33 p.m.