Triple
T17484773
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kapellmeister of the Leipzig Gewandhaus Orchestra |
E425750
|
entity |
| Predicate | typicalAppointmentType |
P12376
|
FINISHED |
| Object | long-term tenure |
—
|
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: long-term tenure | Statement: [Kapellmeister of the Leipzig Gewandhaus Orchestra, typicalAppointmentType, long-term tenure]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAppointmentType Context triple: [Kapellmeister of the Leipzig Gewandhaus Orchestra, typicalAppointmentType, long-term tenure]
-
A.
typicalAppointment
Indicates that an appointment represents a standard, usual, or commonly occurring scheduling arrangement between entities.
-
B.
typicalAppointmentContext
Indicates the usual situational setting or circumstances in which an appointment typically occurs.
-
C.
appointmentType
chosen
Indicates the specific category or nature of an appointment associated with an entity or event.
-
D.
typeOfAppointmentBody
Indicates the specific category or kind of appointment being referenced or scheduled.
-
E.
typicalVisitType
Indicates the usual or most common category of visit associated with an entity 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_69d889dccf7481909264a1844a2e9100 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e451d13e208190a187b5a08fd2b5d5 |
completed | April 19, 2026, 3:53 a.m. |
| PD | Predicate disambiguation | batch_69e3b4f341c88190adabe526d8903b05 |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:48 a.m.