Triple
T20448322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baby Fat: Act 1 |
E501575
|
entity |
| Predicate | hasTheatricalInfluence |
P130631
|
FINISHED |
| Object | stage 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: stage musical | Statement: [Baby Fat: Act 1, hasTheatricalInfluence, stage musical]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTheatricalInfluence Context triple: [Baby Fat: Act 1, hasTheatricalInfluence, stage musical]
-
A.
wereInfluencedBy
Indicates that one entity’s ideas, actions, or characteristics were shaped or affected by another entity.
-
B.
cinemaInfluence
Indicates how one entity affects or shapes another through the medium of cinema, such as films, filmmaking, or cinematic culture.
-
C.
hasTheatricalStyle
chosen
Indicates that one entity possesses, exhibits, or is characterized by a particular theatrical style associated with another entity.
-
D.
hasGenreInfluenceOn
Indicates that one genre has a notable impact on shaping or influencing the characteristics, style, or development of another genre.
-
E.
hasRetroInfluence
Indicates that something is influenced by or incorporates stylistic or conceptual elements from an earlier time or past era.
- 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_69e0b4ac0a1c81908845d0f8a56abce8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e68cff1bcc8190bfc843686be117cb |
completed | April 20, 2026, 8:30 p.m. |
| PD | Predicate disambiguation | batch_69e57679eb40819086142df3e39c928e |
completed | April 20, 2026, 12:42 a.m. |
Created at: April 16, 2026, 11:32 a.m.