Triple
T4268529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Captain general |
E96882
|
entity |
| Predicate | typicalAppointer |
P54125
|
FINISHED |
| Object | monarch |
—
|
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: monarch | Statement: [Captain general, typicalAppointer, monarch]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAppointer Context triple: [Captain general, typicalAppointer, monarch]
-
A.
typicalAppointment
Indicates that an appointment represents a standard, usual, or commonly occurring scheduling arrangement between entities.
-
B.
hasAppointer
chosen
Indicates that one entity is responsible for appointing or assigning another entity to a role, position, or function.
-
C.
appointmentType
Indicates the specific category or nature of an appointment associated with an entity or event.
-
D.
appointmentBy
Indicates that one entity is appointed or designated to a role, position, or task by another entity.
-
E.
typicalRegister
Indicates the usual or most common linguistic register (e.g., formal, informal, technical) in which something—such as a word, expression, or communication—is typically used.
- 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_69b34543f06c8190915ebb1a4574ffa9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34ff913608190b6ccf4a85057b07b |
completed | March 12, 2026, 11:44 p.m. |
| PD | Predicate disambiguation | batch_69b347f8dcb08190a725c1f7fb5a7466 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:07 p.m.