Triple
T17364164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marechal (Portugal) |
E422143
|
entity |
| Predicate | typicalAppointmentContext |
P127202
|
FINISHED |
| Object | extraordinary military merit |
—
|
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: extraordinary military merit | Statement: [Marechal (Portugal), typicalAppointmentContext, extraordinary military merit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAppointmentContext Context triple: [Marechal (Portugal), typicalAppointmentContext, extraordinary military merit]
-
A.
typicalAppointment
Indicates that an appointment represents a standard, usual, or commonly occurring scheduling arrangement between entities.
-
B.
appointmentType
Indicates the specific category or nature of an appointment associated with an entity or event.
-
C.
recommendationBodyForAppointments
Indicates that a recommendation’s content or message is specifically intended for use in the context of appointments.
-
D.
typeOfAppointmentBody
Indicates the specific category or kind of appointment being referenced or scheduled.
-
E.
reasonForAppointment
Indicates the underlying purpose or cause for which an appointment is scheduled or taking place.
- F. None of above. chosen
Provenance (4 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_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a4f52988190847230e119a35b87 |
completed | April 19, 2026, 2:13 a.m. |
| PD | Predicate disambiguation | batch_69e3b02662d08190a07d0fb5c04b6f33 |
completed | April 18, 2026, 4:24 p.m. |
| PDg | Predicate description generation | batch_69e3b2a33e8481908fa6ef45290d08aa |
completed | April 18, 2026, 4:34 p.m. |
Created at: April 10, 2026, 5:44 a.m.