Triple
T13816163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | the Prince |
E332024
|
entity |
| Predicate | relationshipToOtherArchetype |
P38921
|
FINISHED |
| Object | love interest of the 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: love interest of the Princess | Statement: [the Prince, relationshipToOtherArchetype, love interest of the Princess]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToOtherArchetype Context triple: [the Prince, relationshipToOtherArchetype, love interest of the Princess]
-
A.
relationshipToOtherSefirot
Indicates how one sefirah is connected or related to other sefirot within the overall structure or network of sefirot.
-
B.
relationshipCharacterizedAs
Indicates that one relationship is described, defined, or typified in terms of another specified characteristic or relational type.
-
C.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
D.
relationshipToCharacter
chosen
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
E.
relatedType
Indicates that one entity is connected to another through a specified type or category of relationship.
- 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_69d81c59f8808190a851bc56afdc55e9 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02806e148190996f58934e66d7d8 |
completed | April 14, 2026, 9:01 a.m. |
| PD | Predicate disambiguation | batch_69dbc862e9608190bd8a3d883959b7e4 |
completed | April 12, 2026, 4:29 p.m. |
Created at: April 9, 2026, 10:12 p.m.