Triple
T22046096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colombina |
E544767
|
entity |
| Predicate | relationshipTypeWithHarlequin |
P10690
|
FINISHED |
| Object | witty and resourceful lover |
—
|
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: witty and resourceful lover | Statement: [Colombina, relationshipTypeWithHarlequin, witty and resourceful lover]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithHarlequin Context triple: [Colombina, relationshipTypeWithHarlequin, witty and resourceful lover]
-
A.
literaryRelationship
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
-
B.
relationshipType
chosen
Indicates the specific kind of relationship that exists between two or more entities.
-
C.
relationshipCharacterizedAs
Indicates that one relationship is described, defined, or typified in terms of another specified characteristic or relational type.
-
D.
fictionalRelationship
Indicates a relationship that exists only within a fictional or imagined context between entities.
-
E.
hasRelationshipTypeWith Alexandra Bergson
Indicates that there exists a specific type or category of relationship between an entity and Alexandra Bergson.
- 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_69e11e32445c8190ab97089b48a130bb |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1282f4a448190bca55348c457a4bd |
completed | April 28, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69e6f643ca74819083e8ab78e843f243 |
completed | April 21, 2026, 4 a.m. |
Created at: April 16, 2026, 8:26 p.m.