Triple
T1684430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Princesse de Babylone |
E36409
|
entity |
| Predicate | hasProtagonistOrigin |
P28569
|
FINISHED |
| Object | Babylonian princess |
—
|
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: Babylonian princess | Statement: [La Princesse de Babylone, hasProtagonistOrigin, Babylonian princess]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProtagonistOrigin Context triple: [La Princesse de Babylone, hasProtagonistOrigin, Babylonian princess]
-
A.
protagonistOrigin
chosen
Indicates that one entity is the origin, source, or starting point of the protagonist in a narrative or story.
-
B.
hasProtagonist
Indicates that a work of narrative has a main character who serves as its central focus or driving agent.
-
C.
mainProtagonist
Indicates that the subject is the central character or primary focus in the narrative of the related work.
-
D.
protagonistBasedOn
Indicates that a fictional work’s main character is modeled on, inspired by, or derived from a particular real or fictional person or entity.
-
E.
hasProtagonistRelationship
Indicates that there exists a central, story-driving relationship involving the protagonist and another entity within a 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_69a886151508819084fa7f1ce6e05577 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aba644070c81908745b56d981fe273 |
completed | March 7, 2026, 4:15 a.m. |
| PD | Predicate disambiguation | batch_69aa61b57a6881909373af287ef24799 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:29 p.m.