Triple
T12182321
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saxon Seats |
E290249
|
entity |
| Predicate | hadMilitaryObligation |
P46563
|
FINISHED |
| Object | defense of Transylvanian borders |
—
|
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: defense of Transylvanian borders | Statement: [Saxon Seats, hadMilitaryObligation, defense of Transylvanian borders]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadMilitaryObligation Context triple: [Saxon Seats, hadMilitaryObligation, defense of Transylvanian borders]
-
A.
hadMilitaryObligationsTo
chosen
Indicates that one party was bound by duty or law to provide military service, support, or protection to another party.
-
B.
hadMilitaryPost
Indicates that an entity held an official position or assignment within a military organization.
-
C.
hasMilitaryStatus
Indicates that an entity possesses a specific military affiliation, role, or status (such as active duty, reserve, or veteran).
-
D.
militaryStatus
Indicates the relationship between an entity and a military organization in terms of service condition, such as active duty, reserve, veteran, or non-military status.
-
E.
militaryBackground
Indicates that an entity has prior or current experience, service, or training in a military organization.
- 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_69d6ab64de5881908d56eb7a75c6cc69 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91a83012c81908d04bbab5fdcd8c2 |
completed | April 10, 2026, 3:42 p.m. |
| PD | Predicate disambiguation | batch_69d91510a258819090ef8fbdc2d8707b |
completed | April 10, 2026, 3:19 p.m. |
Created at: April 8, 2026, 9:50 p.m.