Triple
T14736873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hans Spialek |
E346233
|
entity |
| Predicate | musicSpecialization |
P81309
|
FINISHED |
| Object | orchestration for musical theatre |
—
|
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: orchestration for musical theatre | Statement: [Hans Spialek, musicSpecialization, orchestration for musical theatre]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: musicSpecialization Context triple: [Hans Spialek, musicSpecialization, orchestration for musical theatre]
-
A.
genreSpecialty
Indicates that an entity specializes in or is particularly associated with a specific genre.
-
B.
musicField
chosen
Indicates a relationship where an entity is associated with a particular field, genre, or domain within music.
-
C.
musicBy
Indicates that a piece of music, performance, or recording is created, composed, or performed by a specified musical artist or group.
-
D.
musicalAttribute
Indicates a relationship where a musical work, performance, or element is characterized by a specific musical property or quality (such as tempo, key, style, or mood).
-
E.
styleOfMusic
Indicates the musical genre or stylistic category that characterizes a piece of music, artist, or performance.
- 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_69d822e6f1c88190bc494d491a907114 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec73114cc819088e1101b689fc70b |
completed | April 14, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69de8bf9331481909582045cd567d91f |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:29 a.m.