Triple
T8969488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marianne Beauséjour |
E214226
|
entity |
| Predicate | allegianceInStory |
P1201
|
FINISHED |
| Object | Allies |
—
|
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: Allies | Statement: [Marianne Beauséjour, allegianceInStory, Allies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: allegianceInStory Context triple: [Marianne Beauséjour, allegianceInStory, Allies]
-
A.
protagonistAllegiance
Indicates the group, cause, or side with which the main character is aligned or to which they show loyalty.
-
B.
hadAllegiance
Indicates that an entity was loyally committed or formally bound in support or service to another entity, such as a person, group, or cause.
-
C.
allegiance
chosen
Indicates a relationship where one entity is loyal, committed, or obligated in support or service to another entity.
-
D.
opponentForceAllegiance
Indicates that one force, group, or individual is aligned with or belongs to an opposing or enemy side in a conflict or competition.
-
E.
hasAllyInStory
Indicates that one entity is portrayed as an ally or supportive partner of another entity within the context of a specific story or narrative.
- 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_69ca839dbf608190a2f5990477115d29 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6765babc8190a4a3b79aa21047c8 |
completed | April 1, 2026, 12:31 a.m. |
| PD | Predicate disambiguation | batch_69cc5ed9a2d48190ad11381078e823b7 |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:01 p.m.