Triple
T21703947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ethiopian Imperial Guard |
E535724
|
entity |
| Predicate | loyaltyExpectation |
P76252
|
FINISHED |
| Object | personal loyalty to the emperor |
—
|
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: personal loyalty to the emperor | Statement: [Ethiopian Imperial Guard, loyaltyExpectation, personal loyalty to the emperor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: loyaltyExpectation Context triple: [Ethiopian Imperial Guard, loyaltyExpectation, personal loyalty to the emperor]
-
A.
loyaltyDimension
chosen
Indicates the degree or aspect of loyalty characterizing the relationship between entities.
-
B.
isLoyalTo
Indicates that one entity consistently supports, respects, or remains faithful to another entity.
-
C.
loyaltyReason
Indicates the reason or motivation behind one entity’s loyalty or allegiance to another.
-
D.
loyaltyIncentive
Indicates a relationship where benefits or rewards are provided to encourage or recognize continued commitment or repeat engagement.
-
E.
loyaltyGoal
Indicates that one entity has the objective or commitment to remain faithful, supportive, or devoted to another entity or cause.
- 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_69e0c46b44c0819088ab883ebd44e0e8 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef9b82901c81909de242c920fbc164 |
completed | April 27, 2026, 5:23 p.m. |
| PD | Predicate disambiguation | batch_69e6969113cc8190ab69855ef5667e4b |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:46 p.m.